Data interaction platforms utilizing dynamic relational awareness

ABSTRACT

There is a need for more effective and efficient data modeling and/or data visualization solutions. This need can be addressed by, for example, solutions for performing data modeling and/or data visualization in an effective and efficient manner. In one example, solutions for generating a data model with dynamic relational awareness are disclosed. In another example, solutions for processing data retrieval queries using data models with dynamic relational awareness are disclosed. In yet another example, solutions for generating data visualizations using data models with dynamic relational awareness are disclosed. In a further example, solutions for integrating external data objects into data models with dynamic relational awareness are disclosed.

CROSS-REFERENCES TO RELATED APPLICATIONS

The present application claims priority to U.S. Provisional PatentApplication Nos. 62/774,569, 62/774,573, 62/774,579, and 62/774,602, allfiled on Dec. 3, 2018, and all of which are incorporated herein byreference in their entireties.

BACKGROUND

Various embodiments of the present invention address technicalchallenges related to data modeling and/or data visualization. Existingsolutions are ill-suited to efficiently and reliably perform datamodeling and/or data visualization. Various embodiments of the presentaddress the shortcomings of the data modeling and/or data visualizationsolutions and disclose various techniques for efficiently and reliablyperforming data modeling and/or data visualization.

BRIEF SUMMARY

In general, embodiments of the present invention provide methods,apparatus, systems, computing devices, computing entities, and/or thelike for data modeling and/or data visualization. Certain embodimentsutilize systems, methods, and computer program products that performdata modeling and/or data visualization using at least one of objectabsorption scores, relational absorption scores, individual absorptionscores, hierarchical absorption scores, operational absorption scores,environment-based absorption scores, attribute-based absorption scores,data visualization spaces, relationship extrapolation spaces, andrelational score extrapolation spaces.

In accordance with one aspect, a method is provided. In one embodiment,the method comprises: (i) for each data object of a plurality of dataobjects: generating an individual absorption score, wherein theindividual absorption score for the data object indicates an estimatedrelational awareness tendency of the data object given one or moreindividual attributes of the data object; generating a hierarchicalabsorption score, wherein the hierarchical absorption score for the dataobject is determined based at least in part on each individualabsorption score for a parent data object that is a hierarchical parentof the data object; generating an operational absorption score, whereinthe operational absorption score for the data object is determined basedat least in part on each individual absorption score for a related dataobject that is operationally related to the data object; and generatingan overall absorption score based at least in part on the individualabsorption score for the data object, the hierarchical absorption scorefor the data object, and the operational absorption score for the dataobject; (ii) for each data object relationship, generating anenvironment-based absorption score, wherein the environment-basedabsorption score for the data object relationship indicates an estimatedrelational significance of the data object relationship given anenvironment state of the data interaction platform; and (iii) for eachdata object relationship that is associated with a plurality of relateddata objects, generating a relational awareness score for each of theplurality of related data objects associated with the data objectrelationship, wherein the relational awareness score for the data objectrelationship with respect to a particular related data object isdetermined based at least in part on the overall absorption score of theparticular related data object and the environment-based absorptionscore for the data object relationship.

In accordance with another aspect, a computer program product isprovided. The computer program product may comprise at least onecomputer-readable storage medium having computer-readable program codeportions stored therein, the computer-readable program code portionscomprising executable portions configured to: (i) for each data objectof a plurality of data objects: generate an individual absorption score,wherein the individual absorption score for the data object indicates anestimated relational awareness tendency of the data object given one ormore individual attributes of the data object; generate a hierarchicalabsorption score, wherein the hierarchical absorption score for the dataobject is determined based at least in part on each individualabsorption score for a parent data object that is a hierarchical parentof the data object; generate an operational absorption score, whereinthe operational absorption score for the data object is determined basedat least in part on each individual absorption score for a related dataobject that is operationally related to the data object; and generate anoverall absorption score based at least in part on the individualabsorption score for the data object, the hierarchical absorption scorefor the data object, and the operational absorption score for the dataobject; (ii) for each data object relationship, generate anenvironment-based absorption score, wherein the environment-basedabsorption score for the data object relationship indicates an estimatedrelational significance of the data object relationship given anenvironment state of the data interaction platform; and (iii) for eachdata object relationship that is associated with a plurality of relateddata objects, generate a relational awareness score for each of theplurality of related data objects associated with the data objectrelationship, wherein the relational awareness score for the data objectrelationship with respect to a particular related data object isdetermined based at least in part on the overall absorption score of theparticular related data object and the environment-based absorptionscore for the data object relationship.

In accordance with yet another aspect, an apparatus comprising at leastone processor and at least one memory including computer program code isprovided. In one embodiment, the at least one memory and the computerprogram code may be configured to, with the processor, cause theapparatus to: (i) for each data object of a plurality of data objects:generate an individual absorption score, wherein the individualabsorption score for the data object indicates an estimated relationalawareness tendency of the data object given one or more individualattributes of the data object; generate a hierarchical absorption score,wherein the hierarchical absorption score for the data object isdetermined based at least in part on each individual absorption scorefor a parent data object that is a hierarchical parent of the dataobject; generate an operational absorption score, wherein theoperational absorption score for the data object is determined based atleast in part on each individual absorption score for a related dataobject that is operationally related to the data object; and generate anoverall absorption score based at least in part on the individualabsorption score for the data object, the hierarchical absorption scorefor the data object, and the operational absorption score for the dataobject; (ii) for each data object relationship, generate anenvironment-based absorption score, wherein the environment-basedabsorption score for the data object relationship indicates an estimatedrelational significance of the data object relationship given anenvironment state of the data interaction platform; and (iii) for eachdata object relationship that is associated with a plurality of relateddata objects, generate a relational awareness score for each of theplurality of related data objects associated with the data objectrelationship, wherein the relational awareness score for the data objectrelationship with respect to a particular related data object isdetermined based at least in part on the overall absorption score of theparticular related data object and the environment-based absorptionscore for the data object relationship.

BRIEF DESCRIPTION OF THE DRAWINGS

Having thus described the invention in general terms, reference will nowbe made to the accompanying drawings, which are not necessarily drawn toscale, and wherein:

FIG. 1 provides an exemplary overview of an architecture that can beused to practice embodiments of the present invention.

FIG. 2 provides an example data interaction platform computing entity inaccordance with some embodiments discussed herein.

FIG. 3 provides an example client computing entity in accordance withsome embodiments discussed herein.

FIG. 4 provides an operational example of a user interface for a datainteraction platform in accordance with some embodiments discussedherein.

FIG. 5 provides an operational example of a user interface for amulti-object visualization space for various living data objects inaccordance with some embodiments discussed herein.

FIG. 6 provides an operational example of a user interface for aper-object visualization space for a living data object in accordancewith some embodiments discussed herein.

FIG. 7 provides an operational example of a user interface for adding adata object to a data model in accordance with some embodimentsdiscussed herein.

FIG. 8 provides an operational example of a user interface forhierarchical division of actions data objects in accordance with someembodiments discussed herein.

FIG. 9 provides an operational example of a user interface for amulti-object visualization space for various tasks data objects inaccordance with some embodiments discussed herein.

FIG. 10 provides an operational example of a user interface for aper-object visualization space for a tasks data object in accordancewith some embodiments discussed herein.

FIG. 11 provides an operational example of a user interface forhierarchical division of knowledge data objects in accordance with someembodiments discussed herein.

FIG. 12 provides an operational example of a user interface of a fileselection user interface for accessing things data objects in accordancewith some embodiments discussed herein.

FIG. 13 provides an operational example of a user interface that enablesuser selection of environment states for a data interaction platform inaccordance with some embodiments discussed herein.

FIG. 14 provides another operational example of a user interface for amulti-object visualization space for various living data objects inaccordance with some embodiments discussed herein.

FIG. 15 provides an operational example of a user interface fordisplaying data object results of a data retrieval query in accordancewith some embodiments discussed herein.

FIG. 16 provides an operational example of a user interface fordisplaying data object results and data object relationship results of adata retrieval query in accordance with some embodiments discussedherein.

FIG. 17 is a logical data flow diagram for a data interaction systemutilizing dynamic relational awareness in accordance with someembodiments discussed herein.

FIG. 18 is a flowchart diagram of an example process for generatingrelational awareness models for a data interaction platform inaccordance with some embodiments discussed herein.

FIG. 19 is a flowchart diagram of an example process for generating anabsorption score for a particular data object in accordance with someembodiments discussed herein.

FIG. 20 is a flowchart diagram of an example process for generating anindividual absorption score for a particular data object in accordancewith some embodiments discussed herein.

FIG. 21 provides an operational example of a dynamic attribute schema inaccordance with some embodiments discussed herein.

FIG. 22 provides an operational example of a dynamic property schema inaccordance with some embodiments discussed herein.

FIG. 23 provides an operational example of a static attribute schema inaccordance with some embodiments discussed herein.

FIG. 24 provides an operational example of an individual absorptionspace in accordance with some embodiments discussed herein.

FIG. 25 is a flowchart diagram of an example process 2500 for generatinga visual representation of a group of retrieved data objects retrievedin response to a data retrieval query in accordance with someembodiments discussed herein.

FIG. 26 is a flowchart diagram of an example process for generatingvisualization configuration parameters for a particular retrieved dataobject in accordance with some embodiments discussed herein.

FIG. 27 provides an operational example of a visualization configurationuser interface in accordance with some embodiments discussed herein.

FIG. 28 provides an operational example of a display style selectionuser interface element in accordance with some embodiments discussedherein.

FIG. 29 provides an operational example of a cloud layout user interfacein accordance with some embodiments discussed herein.

FIG. 30 provides an operational example of a spiral layout userinterface in accordance with some embodiments discussed herein.

FIG. 31 provides an operational example of a grid layout user interfacein accordance with some embodiments discussed herein.

FIG. 32 provides an operational example of a line layout user interfacein accordance with some embodiments discussed herein.

FIG. 33 provides an operational example of a cube layout user interfacein accordance with some embodiments discussed herein.

FIG. 34 provides an operational example of a visual tools selection userinterface in accordance with some embodiments discussed herein.

FIG. 35 provides an operational example of a data visualization userinterface in accordance with some embodiments discussed herein.

FIG. 36 is a flowchart diagram of an example process for integrating anexternal data object into a data model with dynamic relational awarenessin accordance with some embodiments discussed herein.

FIG. 37 is an operational flow diagram of an example process forextracting unintegrated data object relationships based on analyzing aparticular digital document in accordance with some embodimentsdiscussed herein.

FIG. 38 provides an operational example of another user interface fordisplaying data object results of a data retrieval query in accordancewith some embodiments discussed herein.

FIG. 39 provides an operational example of another user interface for amulti-object visualization space for various tasks data objects inaccordance with some embodiments discussed herein.

DETAILED DESCRIPTION

Various embodiments of the present invention now will be described morefully hereinafter with reference to the accompanying drawings, in whichsome, but not all embodiments of the inventions are shown. Indeed, theseinventions may be embodied in many different forms and should not beconstrued as limited to the embodiments set forth herein; rather, theseembodiments are provided so that this disclosure will satisfy applicablelegal requirements. The term “or” is used herein in both the alternativeand conjunctive sense, unless otherwise indicated. The terms“illustrative” and “exemplary” are used to be examples with noindication of quality level. Like numbers refer to like elementsthroughout. Moreover, while certain embodiments of the present inventionare described with reference to predictive data analysis, one ofordinary skill in the art will recognize that the disclosed concepts canbe used to perform other types of data analysis.

I. OVERVIEW

Various embodiments of the present invention address technicalshortcomings of traditional graph-based databases. For example, variousembodiments of the present invention introduce innovative data modelsthat process relationships between data objects not as staticassociations that are recorded independent of those data objects, but asdynamic associations that are recorded and absorbed by the data objectsaccording to various attributes of those data objects. According to someaspects, a data object has relational awareness score with respect toeach of its associated data object relationships. This allows the dataobject to have an independent recognition of various data objectrelationships, including data object relationships that are typicallymodeled indirect data object relationships in traditional graph models,while being able to distinguish between more significant data objectrelationships (e.g., data object relationships having higher respectiverelational awareness scores) and less significant data objectrelationships (e.g., data object relationships having lower respectiverelational awareness scores).

In traditional graph-based data models, relationships between dataobjects are processed and recorded as static associations defined byunderlying semantics of data. For example, to show that John is anemployee of a subsidiary of the XYZ company, a traditional graph-basedmodel may record a relationship between John and his employer as well asa relationship between John's employer and the XYZ company. In suchgraph-based data models, depending on the underlying schema definitionfor a data object in the relevant graph-based database, each data object(e.g., the data object corresponding to John) either has no relationalawareness scope (e.g., is simply a data object referenced by arelationship data object) or a relational awareness scope limited todirect relationships (e.g., is simply aware of the “is employed by”relationship with his employer).

In this way, in a traditional graph-based database, the relationalawareness of data objects is at best limited by conceptual semantics ofthe node structure of the graph model. This is despite the fact thatsuch semantics may provide a poor way of modeling functionalsignificance relationships between data objects. The result isineffective databases that fail to perform efficient and effective dataretrieval and/or data visualization. Relatedly, because of the rigidsemantic structure of such databases, they are not very scalable,because effective integration of external data objects requires mappingof such external data objects into a rigid and complex structure ofrestrictive relationships.

Various embodiments of the present invention address the notedshortcomings of the traditional graph-based database by introducing datamodels that process relationships between data objects not as staticassociations that are recorded independent of those data objects, but asdynamic associations that are recorded and absorbed by the data objectsaccording to various attributes of those data objects. For example,various embodiments of the present invention enable recording numerousrelationships between data objects as well as associating each recordedrelationship with a dynamically-generated relational awareness scorethat indicates the significance of relationships to associated dataobjects. As discussed in greater detail below, relational awarenessscores for particular data objects may be determined based at least inpart on individual attributes of the particular data objects, individualattributes of hierarchical parent data objects of the particular dataobjects, individual attributes of data objects that are operationallyrelated to the particular data objects, individual attributes of dataobjects that are deemed sufficiently similar to the particular dataobjects, significance of data objects and/or data object relationshipsto environment spaces of a data interaction environment, and/or thelike.

Through utilizing the dynamic relational awareness concepts describedherein, data retrieval and/or data visualization may be rendered moreefficient and effective. Various embodiments of the present inventionintroduce efficient techniques for data retrieval and/or datavisualization that utilize relational awareness scores and/or absorptionscores. Furthermore, utilizing the dynamic relational awareness conceptsdescribed herein that are more scalable and are better capable ofintegrating external data objects into a relationally aware data modelcompared to existing data management solutions. Various embodiments ofthe present invention introduce efficient techniques for efficiently andeffectively integrating external data objects into relationally awaredata models. Through utilizing at least one of the dynamic relationalawareness concepts described herein, the data retrieval conceptsdiscussed herein, the data visualization concepts described, and theexternal integration concepts described herein, various embodiments ofthe present invention address various shortcomings of existing datamanagement solutions (e.g., various shortcomings of existing graph-baseddata management solutions) and make important technical contributions toefficiency and effectiveness of such data management solutions.

Furthermore, various embodiments of the present invention dynamicallymodify data models (e.g., dynamically define relations between dataobjects and/or relational parameters for relations between data objects)based on an operational environment of an end-user interacting with adata interaction platform. In doing so, the noted embodiments of thepresent invention increase efficiency and user-friendliness of dataretrieval and/or data searching by taking into account environmentand/or contextual considerations in utilizing various relationshipsbetween the data objects to perform the noted data retrieval and/or datasearching operations. The described environmentally dynamic datamodeling techniques enable defining a large number of data objectrelationships at a time prior to a data retrieval session and utilizinga portion of the large number of data object relationships at a time ofthe data retrieval session. In this way, the described environmentallydynamic data modeling techniques differ from static data modelingtechniques utilized in a variety of data modeling environments, such asrelational data modeling environments, graph-based data modelingenvironments, static schema-based data modeling environments, etc.

Moreover, various embodiments of the present invention introduce dynamicdata modeling techniques that enable utilizing machine learning and/orartificial intelligence techniques to set parameters that definerelevance of inter-object relationships for data retrieval and/or datasearching applications. In doing so, the noted embodiments of thepresent invention increase efficiency and user-friendliness of dataretrieval and/or data searching by integrating predictive inferences(e.g., predictive inferences based on past user activities) indetermining relevance of particular inter-object relationships forparticular data retrieval and/or data searching tasks (e.g., particulardata retrieval and/or data searching tasks defined with respect to anoperational environment associated with such tasks). Examples of machinelearning techniques used to infer relationship parameters based on pastuser activity data include techniques that utilize recurrent neuralnetwork models as well as techniques that utilize online learningmodels, such as techniques that utilizes a follow-the-regularized-leaderonline learning model.

II. DEFINITIONS OF CERTAIN TERMS

The term “individual absorption score” may refer to data that indicatean estimated relational awareness tendency of a particular data objectgiven one or more individual attributes of the particular data object.For example, based at least in part on an example model for inferringindividual absorption scores, a data object associated with a particularindividual person having a high educational degree may be deemed to havea high absorption score. As another example, based at least in part onanother example model for generating individual absorption scores, adata object a data object associated with a particular individual personhaving a particular physical profile (e.g., age, height, weight, and/orthe like) may be deemed to have a high absorption score.

The term “hierarchical absorption score” may refer to data that indicatean estimated relational awareness tendency of a particular data objectgiven one or more individual attributes of a parent data object of theparticular data object. In some embodiments, the hierarchical absorptionscore for the data object is determined based at least in part on eachindividual absorption score for a parent data object that is ahierarchical parent of the data object. In some embodiments, the one ormore parent data objects for a particular data object include ahierarchical meta-type of the particular data object, where thehierarchical meta-type of the particular data object indicates whetherthe particular data object is comprising one or more relatedhierarchical meta-type designations of a plurality of predefinedhierarchical meta-type designations. In some embodiments, the pluralityof predefined hierarchical meta-type designations include: a firstpredefined hierarchical meta-type designation associated with livingreal-world entities, a second predefined hierarchical meta-typedesignation associated with non-living-object real-world entities, athird predefined hierarchical meta-type designation associated withlocation-defining real-world entities, a fourth predefined hierarchicalmeta-type designation associated with time-defining real-world entities,a fifth predefined hierarchical meta-type designation associated withcommunication-defining entities, a sixth predefined hierarchicalmeta-type designation associated with group-defining entities, and aseventh predefined hierarchical meta-type designation associated withknowledge-defining entities.

The term “operational absorption score” may refer to data that indicatean estimated relational awareness tendency of a particular data objectgiven one or more individual attributes of at least one data object thatis deemed to be operationally related to (e.g., have a sufficientlystrong relationship with) the particular data object. In someembodiments, the operational absorption score for the data object isdetermined based at least in part on each individual absorption scorefor a related data object that is operationally related to theparticular data object. In some embodiments, a related data object isdeemed related to a particular data object if there is anon-hierarchical relationship between the two data objects. In someembodiments, the one or more related data objects for a particular dataobject of include one or more user-defining objects associated with theparticular data object and one or more access-defining data objectsassociated with the particular data object. In some embodiments, the oneor more user-defining objects associated with the particular data objectinclude one or more primary user-defining objects associated with theparticular data object and one or more collaborator user-definingobjects associated with the particular data object. In some embodiments,the one or more access-defining data objects associated with theparticular data object include one or more sharing space data objectsassociated with the particular data object (e.g., a public sharing spacedata object, a collaborator space object, a shared space object, and/orthe like).

The term “environment-based absorption score” may refer to data thatindicate an estimated relational significance of the particular dataobject relationship given an environment state of the data interactionplatform. In some embodiments, the environment state of the datainteraction platform is selected from a plurality of candidateenvironment states of the data interaction platform. In some of thoseembodiments, the plurality of candidate environment states of the datainteraction platform indicates at least one of the following: one ormore private environment states, one or more professional environmentstates, one or more leisure environment state, and one or more publicenvironment states.

The term “environment state” may refer to data that indicate an inferreduser purpose and/or an indicated user purpose behind usage of a softwareenvironment such as a data interaction platform at a particular time.Environment states may be generated based at least in part onuser-supplied information and/or by performing machine learning analysisof the usages of data at different time intervals and/or in differentlocations. For example, a data interaction platform computing entity mayinfer based at least in part on user interaction data that the user usesseparate groups of data objects at different time intervals and thusconclude that the separate groups of data objects belong to differentenvironments. Moreover, selection of one or more environment states fora particular usage session may be performed based at least in part onexplicit user selection and/or based at least in part on detecting thatthe user is at a time-of-day and/or at a location associated with aparticular environment state. For example, a data interaction platformcomputing entity may infer a “working” environment state for aparticular usage session by a user during working hours and/or while theuser is located at a geographic location of the user's office. Asfurther discussed below, an innovative aspect of the present inventionrelates to utilizing relational awareness signals provided by theenvironment states for usage of a data interaction platform to generaterelational awareness scores for particular data objects. In someembodiments, the environment state of a data interaction platform isselected from a plurality of candidate environment states of the datainteraction platform. In some of those embodiments, the plurality ofcandidate environment states of the data interaction platform indicatesat least one of the following: one or more private environment states,one or more professional environment states, one or more leisureenvironment state, and one or more public environment states.

The term “relational awareness score” may refer to data that indicate anestimated and/or predicted significance of a relationship associatedwith a particular data object to modeling real-world and/or virtualrelationships of the particular data object which a data model seeks tomodel. In some embodiments, relational awareness score for arelationship indicates an estimated and/or predicted priority of arelationship associated with a particular data object when performingdata retrieval and/or data search of data associated with the particulardata object. According to some aspects of the present invention, a dataobject has relational awareness score with respect to each of itsassociated data object relationships. This allows the data object tohave an independent recognition of various data object relationships,including data object relationships that are typically modeled indirectdata object relationships in traditional graph models, while being ableto distinguish between more significant data object relationships (e.g.,data object relationships having higher respective relational awarenessscores) and less significant data object relationships (e.g., dataobject relationships having lower respective relational awarenessscores).

The term “absorption metric” may refer to data that indicate a propertyof the particular data object that is determined based at least in parton the individual attributes for the particular object and that can beused to estimate the individual absorption score of the particular dataobject. In some embodiments, generating the one or more absorptionmetrics based at least in part on the one or more individual attributesincludes selecting a subset of the individual attributes based at leastin part on an input space of an individualized absorption spaceconfigured to generate individual absorption scores for data objectsbased at least in part on absorption metrics for the data objects. Insome embodiments, generating the one or more absorption metrics based atleast in part on the one or more individual attributes includesperforming a dimensionality reduction and/or feature embedding algorithmon the one or more individual attributes.

The term “individual absorption space” may refer to data that indicate aspace configured to relate absorption metrics for various data objectsthat include a first set of data objects with known individualabsorption scores and a second set of data objects with unknownindividual absorption scores. An individual absorption space may beutilized to perform predictive inferences configured to generaterelational awareness scores. The noted predictive inferences may includeone or more supervised machine learning inferences and one or moreunsupervised machine learning inferences, the latter category includingone or more clustering machine learning inferences.

The term “absorption parameter” may refer to data that indicate aparameter used to determine an absorption parameter for the retrieveddata object. Examples of absorption parameters include one or more ofindividual absorption parameters, hierarchical absorption parameters,operational absorption parameters, attribute-based absorptionparameters, environment-based absorption parameters, and/or the like. Insome embodiments, the one or more absorption parameters for a particularretrieved data object may include an individual absorption score for theparticular retrieved data object, and the individual absorption score ofthe particular retrieved data object indicates an estimated relationalawareness capacity of the particular retrieved data object given one ormore object attributes of the particular retrieved data object. In someembodiments, the one or more absorption parameters for a particularretrieved data object include a hierarchical absorption score for theparticular retrieved data object, and the hierarchical absorption scoreof the particular retrieved data object is determined based at least inpart on each individual absorption score for a parent data object thatis a hierarchical parent of the particular retrieved data object. Insome embodiments, the one or more absorption parameters for a particularretrieved data object include an operational absorption score for theparticular retrieved data object, and the operational absorption scoreof the particular retrieved data object is determined based at least inpart on each individual absorption score for a related data object thatis operationally related to the particular retrieved data object. Insome embodiments, the one or more absorption parameters for a particularretrieved data object include an environment-based absorption score forthe particular retrieved data object, and the environment-basedabsorption score of the particular retrieved data object indicates anestimated relational significance of the particular retrieved dataobject to an environment state of a data interaction platform executingthe data retrieval query.

The term “visualization configuration parameter” refers to data thatindicate at least one visual feature of an icon associated with theparticular retrieved data object. The visualization configurationparameters may be defined based on one or more data visualizationprogramming languages and/or one or more visualization definitionfeatures provided using graphical interaction capabilities of anintegrated development environment for data visualization. In someembodiments, the one or more visualization configuration parameters fora particular retrieved data object include one or more visualizationlocation coordinates for the particular retrieved data object, one ormore shape-defining visualization configuration parameters for theparticular retrieved data object, one or more rotation-speed-definingvisualization configuration parameters for the particular retrieved dataobject, one or more color-defining visualization configurationparameters for the particular retrieved data object, one or morelighting/highlight visualization configurations configurationparameters, and one or more pulse-intensity-defining visualizationconfiguration parameters for the particular retrieved data object. Insome embodiments, to generate the visualization configuration parametersfor the retrieved data object, a data visualization engine maps the oneor more absorption parameters for the retrieved data object of theplurality of retrieved data objects to a visualization space comprisingone or more input dimensions associated with the one or more absorptionparameters and one or more output dimensions associated with one or morevisualization configuration parameters and generates the visualizationconfiguration parameters based at least in part on the visualizationspace. In some embodiments, at least some of the visualizationconfiguration parameters are determined based at least in part on userinput.

The term “visualization location coordinate” may refer to data thatindicate a dimension of an n-dimensional visualization space associatedwith a data interaction platform. In some embodiments, the visualizationlocation coordinates include at least one of the following: a firstvisualization location coordinate determined based at least in part oneach particular relational awareness score for any retrieved data objectrelationship between the particular retrieved particular data object andother retrieved data objects, a second visualization location coordinatedetermined based at least in part on each environment-based absorptionscore for any retrieved data object relationship between the particularretrieved data object and the other retrieved data objects; and a thirdvisualization location coordinate determined based at least in part on ahierarchical meta-type of the particular retrieved data object, wherethe hierarchical meta-type of the particular data object may indicatewhether the particular data object is comprising one or more relatedhierarchical meta-type designations of a plurality of predefinedhierarchical meta-type designations.

The term “shape-defining visualization configuration parameter” mayrefer to data that indicate an aspect of a shape of the icon associatedwith the particular retrieved data object. For example, a particularshape-defining visualization configuration parameter may define whetherthe icon for a corresponding retrieved data object is a rectangle, acircle, a cylinder, and/or the like. As another example, a particularshape-defining visualization configuration parameter may define size ofthe icon for a corresponding retrieved data object. As yet anotherexample, a particular shape-defining visualization configurationparameter may define size of an aura region associated with the icon fora corresponding retrieved data object. In some embodiments, at least oneshape-defining visualization configuration parameter for a particularretrieved data object may be determined based at least in part on userinput, e.g., user input defining that an aspect of shape of an iconassociated with the particular retrieved data object should indicate aparticular attribute of the particular retrieved data object.

The term “color-defining visualization configuration parameter” mayrefer to data that indicate an aspect of a color of the icon associatedwith the particular retrieved data object. For example, a particularcolor-defining visualization configuration parameter may define whetherthe icon for a corresponding retrieved data object is red, blue, yellow,and/or the like. As another example, a particular color-definingvisualization configuration parameter may define a color of a spectralregion of an icon associated with the corresponding retrieved dataobject. In some embodiments, at least one color-defining visualizationconfiguration parameter for a particular retrieved data object may bedetermined based at least in part on user input, e.g., user inputdefining that an aspect of color of an icon associated with theparticular retrieved data object should indicate a particular attributeof the particular retrieved data object.

The term “rotation-speed-defining visualization configuration parameter”may refer to data that indicate an aspect of a rotation speed of theicon associated with the particular retrieved data object. For example,a particular rotation-speed-defining visualization configurationparameter may define whether the icon for a corresponding retrieved dataobject rotates faster or slow. As another example, a particularrotation-speed-defining visualization configuration parameter may definewhether an icon associated with the corresponding retrieved data objectrotates at all. In some embodiments, at least onerotation-speed-defining visualization configuration parameter for aparticular retrieved data object may be determined based at least inpart on user input, e.g., user input defining that an aspect of rotationspeed of an icon associated with the particular retrieved data objectshould indicate a particular attribute of the particular retrieved dataobject.

The term “lighting/highlight visualization configuration parameter” mayrefer to data that indicate an aspect of lighting and/or highlight ofthe icon associated with the particular retrieved data object. Forexample, a particular lighting/highlight visualization configurationparameter may define whether the icon for a corresponding retrieved dataobject is light or dark. As another example, a particularlighting/highlight visualization configuration parameter may define adiscrete and/or continuous value for an aspect of lighting and/orhighlight of the icon associated with the particular retrieved dataobject based on one or more properties of the retrieved data objects,e.g., one or more relational awareness scores for relationshipsassociated with the retrieved data object.

The term “pulse-intensity-defining visualization configurationparameter” may refer to data that indicate an aspect of a pulseintensity of the icon associated with the particular retrieved dataobject. For example, a particular pulse-intensity-defining visualizationconfiguration parameter may define whether the icon for a correspondingretrieved data object pulses (e.g., contracts and expands). As anotherexample, a particular pulse-intensity-defining visualizationconfiguration parameter may define whether an icon associated with thecorresponding retrieved data object pulses fast or slow. As yet anotherexample, a particular pulse-intensity-defining visualizationconfiguration parameter may define whether an icon associated with thecorresponding retrieved data object has a smaller or larger range ofcontraction and expansion. In some embodiments, at least onepulse-intensity-defining visualization configuration parameter for aparticular retrieved data object may be determined based at least inpart on user input, e.g., user input defining that an aspect of pulseintensity of an icon associated with the particular retrieved dataobject should indicate a particular attribute of the particularretrieved data object. For example, the current state of a userinterface element may enable a user to depict an indication of a firstname of a “living” data object based at least in part on a pulseintensity of the icon of the “living” data object, e.g., such that anicon for a “living” data object having a first name whose first letterhas alphabetic precedence over other “living” data objects pulsesslower.

The term “layout configuration parameter may refer to data that indicatean aspect of layout of icons associated with the group of retrieved dataobjects. In some of the embodiments that utilize layout configurationparameters to define a layout of the icons associated with the group ofretrieved data objects, one or more visualization location coordinatesinclude one or more layout-based configuration parameters that define alocation of the particular retrieved data object with respect to thedefined layout. In some embodiments, layout configuration parameters aregenerated based at least in part on attributes of data objectrelationships between the group of retrieved data objects. In someembodiments, layout configuration parameters are generated based atleast in part on user input.

The term “object integration parameter” may refer to data that indicatean attribute of the external data object that can be used to infer dataobject relationship between the external data object and at least someof the group of integrated data object relationships. In someembodiments, determining which object attributes can be used forintegration is performing using one or more predictive models, such asone or more machine learning predictive models and/or one or moreartificial intelligence predictive models. In some embodiments, the oneor more object integration parameters for the external data objectinclude one or more entity-type-defining parameters for the externaldata object, such as an entity-type-defining parameter indicating thatthe external data object relates to a “contacts” data object and/or anentity-type-defining parameter indicating that the external data objectrelates to a “projects” data object. In some embodiments, the one ormore object integration parameters for the external data object includeone or more topic-defining parameters for the external data object, suchas a topic-defining parameter indicating that the external data objectrelates to business and/or a topic-defining parameter indicating thatthe external data object relates to sports. In some embodiments, the oneor more object integration parameters are generated using an onlinelearning model configured to process user interaction data with theexternal data object outside of the data interaction platform togenerate the one or more object integration parameters. In someembodiments, the online learning model is afollow-the-regularized-leader model.

The term “unintegrated data object relationship” may refer to data thatindicate a relationship between the external data object and at leastone of the group of integrated data objects. In some embodiments, eachunintegrated data object relationship is associated with one or morerelated data objects, where the one or more related data objectsassociated with an unintegrated data object relationship may include theexternal data object and at least one of the group of integrated dataobjects. In some embodiments, generating the one or more unintegrateddata object relationships includes mapping the one or more objectintegration parameters to a relationship extrapolation space of thegroup of integrated data objects, wherein the relationship extrapolationspace is comprising one or more input dimensions associated with the oneor more object integration parameters and one or more output dimensionsassociated with one or more candidate data object relationship types;and determining the one or more unintegrated data object relationshipsbased at least in part on the relationship extrapolation space.

The term “relationship extrapolation space” may refer to a data objectthat associate input parameters of particular data objects to desiredrelational definition parameters of the particular data objects. In someembodiments, generating the one or more unintegrated data objectrelationships includes mapping the one or more object integrationparameters to a relationship extrapolation space of the group ofintegrated data objects, wherein the relationship extrapolation space iscomprising one or more input dimensions associated with the one or moreobject integration parameters and one or more output dimensionsassociated with one or more candidate data object relationship types;and determining the one or more unintegrated data object relationshipsbased at least in part on the relationship extrapolation space. In someembodiments, determining the one or more unintegrated data objectrelationships based at least in part on the relationship extrapolationspace includes utilizing an unsupervised machine learning model (e.g., aclustering machine learning, a K-nearest-neighbor machine learningmodel, and/or the like) defined by the input space and output space ofthe relationship extrapolation space. In some embodiments, determiningthe one or more unintegrated data object relationships based at least inpart on the relationship extrapolation space includes utilizing asupervised machine learning model (e.g., a neural network machinelearning model) defined by the input space and output space of therelationship extrapolation space. In some embodiments, determining theone or more unintegrated data object relationships based at least inpart on the relationship extrapolation space includes utilizing anonline machine learning model (e.g., a follow-the-regularized-leadermachine learning model) defined by the input space and output space ofthe relationship extrapolation space.

The term “relational score extrapolation space” may refer to a dataobject that associate input parameters of particular data objects and/orparticular data object relationships to desiredrelational-awareness-score-related parameters of the particular dataobjects and/or particular data object relationships. In someembodiments, generating each relational awareness score for anunintegrated data object relationship includes mapping the one or morerelational absorption parameters for the unintegrated data objectrelationship to a relational score extrapolation score for the group ofintegrated data object relationships, wherein the relational scoreextrapolation space is comprising one or more input dimensionsassociated with the one or more relational absorption parameters and oneor more output dimensions associated with one or more relationalawareness parameters; and determining the relational awareness scorebased at least in part on the one or more relational awarenessparameters. In some embodiments, determining each relational awarenessscore for an unintegrated data object relationship based at least inpart on the relational score extrapolation space includes utilizing asupervised machine learning model (e.g., a neural network machinelearning model) defined by the input space and output space of therelationship extrapolation space. In some embodiments, determining eachrelational awareness score for an unintegrated data object relationshipbased at least in part on the relational score extrapolation spaceincludes utilizing a supervised machine learning model (e.g., a neuralnetwork machine learning model) defined by the input space and outputspace of the relationship extrapolation space. In some embodiments,determining each relational awareness score for an unintegrated dataobject relationship based at least in part on the relational scoreextrapolation space includes utilizing an online machine learning model(e.g., a follow-the-regularized-leader machine learning model) definedby the input space and output space of the relationship extrapolationspace.

III. COMPUTER PROGRAM PRODUCTS, METHODS, AND COMPUTING ENTITIES

Embodiments of the present invention may be implemented in various ways,including as computer program products that comprise articles ofmanufacture. Such computer program products may include one or moresoftware components including, for example, software objects, methods,data structures, or the like. A software component may be coded in anyof a variety of programming languages. An illustrative programminglanguage may be a lower-level programming language such as an assemblylanguage associated with a particular hardware architecture and/oroperating system platform. A software component comprising assemblylanguage instructions may require conversion into executable machinecode by an assembler prior to execution by the hardware architectureand/or platform. Another example programming language may be ahigher-level programming language that may be portable across multiplearchitectures. A software component comprising higher-level programminglanguage instructions may require conversion to an intermediaterepresentation by an interpreter or a compiler prior to execution.

Other examples of programming languages include, but are not limited to,a macro language, a shell or command language, a job control language, ascript language, a database query or search language, and/or a reportwriting language. In one or more example embodiments, a softwarecomponent comprising instructions in one of the foregoing examples ofprogramming languages may be executed directly by an operating system orother software component without having to be first transformed intoanother form. A software component may be stored as a file or other datastorage construct. Software components of a similar type or functionallyrelated may be stored together such as, for example, in a particulardirectory, folder, or library. Software components may be static (e.g.,pre-established or fixed) or dynamic (e.g., created or modified at thetime of execution).

A computer program product may include a non-transitorycomputer-readable storage medium storing applications, programs, programmodules, scripts, source code, program code, object code, byte code,compiled code, interpreted code, machine code, executable instructions,and/or the like (also referred to herein as executable instructions,instructions for execution, computer program products, program code,and/or similar terms used herein interchangeably). Such non-transitorycomputer-readable storage media include all computer-readable media(including volatile and non-volatile media).

In one embodiment, a non-volatile computer-readable storage medium mayinclude a floppy disk, flexible disk, hard disk, solid-state storage(SSS) (e.g., a solid state drive (SSD), solid state card (SSC), solidstate module (SSM), enterprise flash drive, magnetic tape, or any othernon-transitory magnetic medium, and/or the like. A non-volatilecomputer-readable storage medium may also include a punch card, papertape, optical mark sheet (or any other physical medium with patterns ofholes or other optically recognizable indicia), compact disc read onlymemory (CD-ROM), compact disc-rewritable (CD-RW), digital versatile disc(DVD), Blu-ray disc (BD), any other non-transitory optical medium,and/or the like. Such a non-volatile computer-readable storage mediummay also include read-only memory (ROM), programmable read-only memory(PROM), erasable programmable read-only memory (EPROM), electricallyerasable programmable read-only memory (EEPROM), flash memory (e.g.,Serial, NAND, NOR, and/or the like), multimedia memory cards (MMC),secure digital (SD) memory cards, SmartMedia cards, CompactFlash (CF)cards, Memory Sticks, and/or the like. Further, a non-volatilecomputer-readable storage medium may also include conductive-bridgingrandom access memory (CBRAM), phase-change random access memory (PRAM),ferroelectric random-access memory (FeRAM), non-volatile random-accessmemory (NVRAM), magnetoresistive random-access memory (MRAM), resistiverandom-access memory (RRAM), Silicon-Oxide-Nitride-Oxide-Silicon memory(SONOS), floating junction gate random access memory (FJG RAM),Millipede memory, racetrack memory, and/or the like.

In one embodiment, a volatile computer-readable storage medium mayinclude random access memory (RAM), dynamic random access memory (DRAM),static random access memory (SRAM), fast page mode dynamic random accessmemory (FPM DRAM), extended data-out dynamic random access memory (EDODRAM), synchronous dynamic random access memory (SDRAM), double datarate synchronous dynamic random access memory (DDR SDRAM), double datarate type two synchronous dynamic random access memory (DDR2 SDRAM),double data rate type three synchronous dynamic random access memory(DDR3 SDRAM), Rambus dynamic random access memory (RDRAM), TwinTransistor RAM (TTRAM), Thyristor RAM (T-RAM), Zero-capacitor (Z-RAM),Rambus in-line memory module (RIMM), dual in-line memory module (DIMM),single in-line memory module (SIMM), video random access memory (VRAM),cache memory (including various levels), flash memory, register memory,and/or the like. It will be appreciated that where embodiments aredescribed to use a computer-readable storage medium, other types ofcomputer-readable storage media may be substituted for or used inaddition to the computer-readable storage media described above.

As should be appreciated, various embodiments of the present inventionmay also be implemented as methods, apparatus, systems, computingdevices, computing entities, and/or the like. As such, embodiments ofthe present invention may take the form of an apparatus, system,computing device, computing entity, and/or the like executinginstructions stored on a computer-readable storage medium to performcertain steps or operations. Thus, embodiments of the present inventionmay also take the form of an entirely hardware embodiment, an entirelycomputer program product embodiment, and/or an embodiment that comprisescombination of computer program products and hardware performing certainsteps or operations.

Embodiments of the present invention are described below with referenceto block diagrams and flowchart illustrations. Thus, it should beunderstood that each block of the block diagrams and flowchartillustrations may be implemented in the form of a computer programproduct, an entirely hardware embodiment, a combination of hardware andcomputer program products, and/or apparatus, systems, computing devices,computing entities, and/or the like carrying out instructions,operations, steps, and similar words used interchangeably (e.g., theexecutable instructions, instructions for execution, program code,and/or the like) on a computer-readable storage medium for execution.For example, retrieval, loading, and execution of code may be performedsequentially such that one instruction is retrieved, loaded, andexecuted at a time. In some exemplary embodiments, retrieval, loading,and/or execution may be performed in parallel such that multipleinstructions are retrieved, loaded, and/or executed together. Thus, suchembodiments can produce specifically-configured machines performing thesteps or operations specified in the block diagrams and flowchartillustrations. Accordingly, the block diagrams and flowchartillustrations support various combinations of embodiments for performingthe specified instructions, operations, or steps.

IV. EXEMPLARY SYSTEM ARCHITECTURE

FIG. 1 is a schematic diagram of an example architecture 100 forperforming data modeling and/or data visualization. The architecture 100includes one or more client computing entities 102 and a datainteraction platform computing entity 106. The data interaction platformcomputing entity 106 may be configured to communicate with at least oneof the client computing entities 102 over a communication network (notshown). The communication network may include any wired or wirelesscommunication network including, for example, a wired or wireless localarea network (LAN), personal area network (PAN), metropolitan areanetwork (MAN), wide area network (WAN), or the like, as well as anyhardware, software and/or firmware required to implement it (such as,e.g., network routers, and/or the like). While not depicted in FIG. 1,the data interaction platform computing entity 106 may retrieve inputdata from one or more external computing entities, such as one or moreexternal information server computing entities.

A client computing entity 102 may be configured to provide dataretrieval requests and/or data visualization requests to the datainteraction platform computing entity 106. The data interaction platformcomputing entity 106 may be configured to generate data retrievaloutputs and/or data vitalization outputs in response to data retrievalrequests and/or data visualization requests by client computing entities102 and provide the generated data retrieval outputs and/or datavitalization outputs to requesting client computing entities 102.

The data interaction platform computing entity 106 includes a relationalawareness modeling engine 111, a query processing engine 112, a datavisualization engine 113, an external integration engine 114, and astorage subsystem 108. The relational awareness modeling engine 111 maybe configured to generate relational awareness metadata and/orrelationally aware data models for data objects stored in the storagesubsystem 108 and to store the generated relational awareness metadataand/or relationally aware data models in the storage subsystem 108. Thequery processing engine 112 may be configured to process search queriesbased at least in part on the relational awareness metadata and/orrelationally aware data models stored in the storage subsystem 108. Thedata visualization engine 113 may be configured to generate visualrepresentations of data objects and/or data object relationships basedat least in part on the relational awareness metadata and/orrelationally aware data models stored in the storage subsystem 108. Theexternal integration engine 114 may be configured to generate relationalawareness metadata for external data objects and/or integrate externaldata objects in the relationally aware data models stored in the storagesubsystem 108. The external integration engine 108 may store itsgenerated relational awareness metadata and/or external integration datain the storage subsystem 108. In some embodiments, a visualrepresentation may include one or more of a presentation, a display, auser interface, mark-up data, data that can be utilized to generate auser interface (e.g., JavaScript source code data), a video, etc.

The storage subsystem 108 may be configured to store generatedrelational awareness metadata and/or relationally aware data modelsassociated with the data interaction platform computing entity 108. Thestorage subsystem 108 may include one or more storage units, such asmultiple distributed storage units that are connected through a computernetwork. Each storage unit in the storage subsystem 108 may store atleast one of one or more data assets and/or one or more data about thecomputed properties of one or more data assets. Moreover, each storageunit in the storage subsystem 108 may include one or more non-volatilestorage or memory media including but not limited to hard disks, ROM,PROM, EPROM, EEPROM, flash memory, MMCs, SD memory cards, Memory Sticks,CBRAM, PRAM, FeRAM, NVRAM, MRAM, RRAM, SONOS, FJG RAM, Millipede memory,racetrack memory, and/or the like.

Exemplary Data Interaction Platform Computing Entity

FIG. 2 provides a schematic of a data interaction platform computingentity 106 according to one embodiment of the present invention. Ingeneral, the terms computing entity, computer, entity, device, system,and/or similar words used herein interchangeably may refer to, forexample, one or more computers, computing entities, desktops, mobilephones, tablets, phablets, notebooks, laptops, distributed systems,kiosks, input terminals, servers or server networks, blades, gateways,switches, processing devices, processing entities, set-top boxes,relays, routers, network access points, base stations, the like, and/orany combination of devices or entities adapted to perform the functions,operations, and/or processes described herein. Such functions,operations, and/or processes may include, for example, transmitting,receiving, operating on, processing, displaying, storing, determining,creating/generating, monitoring, evaluating, comparing, and/or similarterms used herein interchangeably. In one embodiment, these functions,operations, and/or processes can be performed on data, content,information, and/or similar terms used herein interchangeably.

As indicated, in one embodiment, the data interaction platform computingentity 106 may also include one or more communications interfaces 220for communicating with various computing entities, such as bycommunicating data, content, information, and/or similar terms usedherein interchangeably that can be transmitted, received, operated on,processed, displayed, stored, and/or the like. In some embodiments, thedata interaction platform computing entity 106 may be configured toperform one or more edge computing capabilities.

As shown in FIG. 2, in one embodiment, the data interaction platformcomputing entity 106 may include or be in communication with one or moreprocessing elements 205 (also referred to as processors, processingcircuitry, and/or similar terms used herein interchangeably) thatcommunicate with other elements within the data interaction platformcomputing entity 106 via a bus, for example. As will be understood, theprocessing element 205 may be embodied in a number of different ways.For example, the processing element 205 may be embodied as one or morecomplex programmable logic devices (CPLDs), microprocessors, multi-coreprocessors, coprocessing entities, application-specific instruction-setprocessors (ASIPs), microcontrollers, and/or controllers. Further, theprocessing element 205 may be embodied as one or more other processingdevices or circuitry. The term circuitry may refer to an entirelyhardware embodiment or a combination of hardware and computer programproducts. Thus, the processing element 205 may be embodied as integratedcircuits, application specific integrated circuits (ASICs), fieldprogrammable gate arrays (FPGAs), programmable logic arrays (PLAs),hardware accelerators, other circuitry, and/or the like. As willtherefore be understood, the processing element 205 may be configuredfor a particular use or configured to execute instructions stored involatile or non-volatile media or otherwise accessible to the processingelement 205. As such, whether configured by hardware or computer programproducts, or by a combination thereof, the processing element 205 may becapable of performing steps or operations according to embodiments ofthe present invention when configured accordingly.

In one embodiment, the data interaction platform computing entity 106may further include or be in communication with non-volatile media (alsoreferred to as non-volatile storage, memory, memory storage, memorycircuitry and/or similar terms used herein interchangeably). In oneembodiment, the non-volatile storage or memory may include one or morenon-volatile storage or memory media 210, including but not limited tohard disks, ROM, PROM, EPROM, EEPROM, flash memory, MMCs, SD memorycards, Memory Sticks, CBRAM, PRAM, FeRAM, NVRAM, MRAM, RRAM, SONOS, FJGRAM, Millipede memory, racetrack memory, and/or the like. As will berecognized, the non-volatile storage or memory media may storedatabases, database instances, database management systems, data,applications, programs, program modules, scripts, source code, objectcode, byte code, compiled code, interpreted code, machine code,executable instructions, and/or the like. The term database, databaseinstance, database management system, and/or similar terms used hereininterchangeably may refer to a collection of records or data that isstored in a computer-readable storage medium using one or more databasemodels, such as a hierarchical database model, network model, relationalmodel, entity—relationship model, object model, document model, semanticmodel, graph model, and/or the like.

In one embodiment, the data interaction platform computing entity 106may further include or be in communication with volatile media (alsoreferred to as volatile storage, memory, memory storage, memorycircuitry and/or similar terms used herein interchangeably). In oneembodiment, the volatile storage or memory may also include one or morevolatile storage or memory media 215, including but not limited to RAM,DRAM, SRAM, FPM DRAM, EDO DRAM, SDRAM, DDR SDRAM, DDR2 SDRAM, DDR3SDRAM, RDRAM, TTRAM, T-RAM, Z-RAM, RIMM, DIMM, SIMM, VRAM, cache memory,register memory, and/or the like. As will be recognized, the volatilestorage or memory media may be used to store at least portions of thedatabases, database instances, database management systems, data,applications, programs, program modules, scripts, source code, objectcode, byte code, compiled code, interpreted code, machine code,executable instructions, and/or the like being executed by, for example,the processing element 205. Thus, the databases, database instances,database management systems, data, applications, programs, programmodules, scripts, source code, object code, byte code, compiled code,interpreted code, machine code, executable instructions, and/or the likemay be used to control certain aspects of the operation of the datainteraction platform computing entity 106 with the assistance of theprocessing element 205 and operating system.

As indicated, in one embodiment, the data interaction platform computingentity 106 may also include one or more communications interfaces 220for communicating with various computing entities, such as bycommunicating data, content, information, and/or similar terms usedherein interchangeably that can be transmitted, received, operated on,processed, displayed, stored, and/or the like. Such communication may beexecuted using a wired data transmission protocol, such as fiberdistributed data interface (FDDI), digital subscriber line (DSL),Ethernet, asynchronous transfer mode (ATM), frame relay, data over cableservice interface specification (DOCSIS), or any other wiredtransmission protocol. Similarly, the data interaction platformcomputing entity 106 may be configured to communicate via wirelessexternal communication networks using any of a variety of protocols,such as general packet radio service (GPRS), Universal MobileTelecommunications System (UMTS), Code Division Multiple Access 2000(CDMA2000), CDMA2000 1× (1×RTT), Wideband Code Division Multiple Access(WCDMA), Global System for Mobile Communications (GSM), Enhanced Datarates for GSM Evolution (EDGE), Time Division-Synchronous Code DivisionMultiple Access (TD-SCDMA), Long Term Evolution (LTE), Evolved UniversalTerrestrial Radio Access Network (E-UTRAN), Evolution-Data Optimized(EVDO), High Speed Packet Access (HSPA), High-Speed Downlink PacketAccess (HSDPA), IEEE 802.11 (Wi-Fi), Wi-Fi Direct, 802.16 (WiMAX),ultra-wideband (UWB), infrared (IR) protocols, near field communication(NFC) protocols, Wibree, Bluetooth protocols, wireless universal serialbus (USB) protocols, and/or any other wireless protocol.

Although not shown, the data interaction platform computing entity 106may include or be in communication with one or more input elements, suchas a keyboard input, a mouse input, a touch screen/display input, motioninput, movement input, audio input, pointing device input, joystickinput, keypad input, and/or the like. The data interaction platformcomputing entity 106 may also include or be in communication with one ormore output elements (not shown), such as audio output, video output,screen/display output, motion output, movement output, and/or the like.

Exemplary Client Computing Entity

FIG. 3 provides an illustrative schematic representative of a clientcomputing entity 102 that can be used in conjunction with embodiments ofthe present invention. In general, the terms device, system, computingentity, entity, and/or similar words used herein interchangeably mayrefer to, for example, one or more computers, computing entities,desktops, mobile phones, tablets, phablets, notebooks, laptops,distributed systems, kiosks, input terminals, servers or servernetworks, blades, gateways, switches, processing devices, processingentities, set-top boxes, relays, routers, network access points, basestations, the like, and/or any combination of devices or entitiesadapted to perform the functions, operations, and/or processes describedherein. Client computing entities 102 can be operated by variousparties. As shown in FIG. 3, the client computing entity 102 can includean antenna 312, a transmitter 304 (e.g., radio), a receiver 306 (e.g.,radio), and a processing element 308 (e.g., CPLDs, microprocessors,multi-core processors, coprocessing entities, ASIPs, microcontrollers,and/or controllers) that provides signals to and receives signals fromthe transmitter 304 and receiver 306, correspondingly.

The signals provided to and received from the transmitter 304 and thereceiver 306, correspondingly, may include signaling information/data inaccordance with air interface standards of applicable wireless systems.In this regard, the client computing entity 102 may be capable ofoperating with one or more air interface standards, communicationprotocols, modulation types, and access types. More particularly, theclient computing entity 102 may operate in accordance with any of anumber of wireless communication standards and protocols, such as thosedescribed above with regard to the data interaction platform computingentity 106. In a particular embodiment, the client computing entity 102may operate in accordance with multiple wireless communication standardsand protocols, such as UMTS, CDMA2000, 1×RTT, WCDMA, GSM, EDGE,TD-SCDMA, LTE, E-UTRAN, EVDO, HSPA, HSDPA, Wi-Fi, Wi-Fi Direct, WiMAX,UWB, IR, NFC, Bluetooth, USB, and/or the like. Similarly, the clientcomputing entity 102 may operate in accordance with multiple wiredcommunication standards and protocols, such as those described abovewith regard to the data interaction platform computing entity 106 via anetwork interface 320.

Via these communication standards and protocols, the client computingentity 102 can communicate with various other entities using conceptssuch as Unstructured Supplementary Service Data (USSD), Short MessageService (SMS), Multimedia Messaging Service (MIMS), Dual-ToneMulti-Frequency Signaling (DTMF), and/or Subscriber Identity ModuleDialer (SIM dialer). The client computing entity 102 can also downloadchanges, add-ons, and updates, for instance, to its firmware, software(e.g., including executable instructions, applications, programmodules), and operating system.

According to one embodiment, the client computing entity 102 may includelocation determining aspects, devices, modules, functionalities, and/orsimilar words used herein interchangeably. For example, the clientcomputing entity 102 may include outdoor positioning aspects, such as alocation module adapted to acquire, for example, latitude, longitude,altitude, geocode, course, direction, heading, speed, universal time(UTC), date, and/or various other information/data. In one embodiment,the location module can acquire data, sometimes known as ephemeris data,by identifying the number of satellites in view and the relativepositions of those satellites (e.g., using global positioning systems(GPS)). The satellites may be a variety of different satellites,including Low Earth Orbit (LEO) satellite systems, Department of Defense(DOD) satellite systems, the European Union Galileo positioning systems,the Chinese Compass navigation systems, Indian Regional Navigationalsatellite systems, and/or the like. This data can be collected using avariety of coordinate systems, such as the Decimal Degrees (DD);Degrees, Minutes, Seconds (DMS); Universal Transverse Mercator (UTM);Universal Polar Stereographic (UPS) coordinate systems; and/or the like.Alternatively, the location information/data can be determined bytriangulating the client computing entity's 102 position in connectionwith a variety of other systems, including cellular towers, Wi-Fi accesspoints, and/or the like. Similarly, the client computing entity 102 mayinclude indoor positioning aspects, such as a location module adapted toacquire, for example, latitude, longitude, altitude, geocode, course,direction, heading, speed, time, date, and/or various otherinformation/data. Some of the indoor systems may use various position orlocation technologies including RFID tags, indoor beacons ortransmitters, Wi-Fi access points, cellular towers, nearby computingdevices (e.g., smartphones, laptops) and/or the like. For instance, suchtechnologies may include the iBeacons, Gimbal proximity beacons,Bluetooth Low Energy (BLE) transmitters, NFC transmitters, and/or thelike. These indoor positioning aspects can be used in a variety ofsettings to determine the location of someone or something to withininches or centimeters.

The client computing entity 102 may also comprise a user interface (thatcan include a display 316 coupled to a processing element 308) and/or auser input interface (coupled to a processing element 308). For example,the user interface may be a user application, browser, user interface,and/or similar words used herein interchangeably executing on and/oraccessible via the client computing entity 102 to interact with and/orcause display of information/data from the data interaction platformcomputing entity 106, as described herein. The user input interface cancomprise any of a number of devices or interfaces allowing the clientcomputing entity 102 to receive data, such as a keypad 318 (hard orsoft), a touch display, voice/speech or motion interfaces, or otherinput device. In embodiments including a keypad 318, the keypad 318 caninclude (or cause display of) the conventional numeric (0-9) and relatedkeys (#, *), and other keys used for operating the client computingentity 102 and may include a full set of alphabetic keys or set of keysthat may be activated to provide a full set of alphanumeric keys. Inaddition to providing input, the user input interface can be used, forexample, to activate or deactivate certain functions, such as screensavers and/or sleep modes.

The client computing entity 102 can also include volatile storage ormemory 322 and/or non-volatile storage or memory 324, which can beembedded and/or may be removable. For example, the non-volatile memorymay be ROM, PROM, EPROM, EEPROM, flash memory, MMCs, SD memory cards,Memory Sticks, CBRAM, PRAM, FeRAM, NVRAM, MRAM, RRAM, SONOS, FJG RAM,Millipede memory, racetrack memory, and/or the like.

The volatile memory may be RAM, DRAM, SRAM, FPM DRAM, EDO DRAM, SDRAM,DDR SDRAM, DDR2 SDRAM, DDR3 SDRAM, RDRAM, TTRAM, T-RAM, Z-RAM, RIMM,DIMM, SIMM, VRAM, cache memory, register memory, and/or the like. Thevolatile and non-volatile storage or memory can store databases,database instances, database management systems, data, applications,programs, program modules, scripts, source code, object code, byte code,compiled code, interpreted code, machine code, executable instructions,and/or the like to implement the functions of the client computingentity 102. As indicated, this may include a user application that isresident on the entity or accessible through a browser or other userinterface for communicating with the data interaction platform computingentity 106 and/or various other computing entities.

In another embodiment, the client computing entity 102 may include oneor more components or functionality that are the same or similar tothose of the data interaction platform computing entity 106, asdescribed in greater detail above. As will be recognized, thesearchitectures and descriptions are provided for exemplary purposes onlyand are not limiting to the various embodiments.

In various embodiments, the client computing entity 102 may be embodiedas an artificial intelligence (AI) computing entity, such as an AmazonEcho, Amazon Echo Dot, Amazon Show, Google Home, and/or the like.Accordingly, the client computing entity 102 may be configured toprovide and/or receive information/data from a user via an input/outputmechanism, such as a display, a camera, a speaker, a voice-activatedinput, and/or the like. In certain embodiments, an AI computing entitymay comprise one or more predefined and executable program algorithmsstored within an onboard memory storage module, and/or accessible over anetwork. In various embodiments, the AI computing entity may beconfigured to retrieve and/or execute one or more of the predefinedprogram algorithms upon the occurrence of a predefined trigger event.

V. EXEMPLARY SYSTEM OPERATIONS

Various embodiments of the present invention address technicalshortcomings of traditional graph-based databases. For example, variousembodiments of the present invention introduce innovative data modelsthat process relationships between data objects not as staticassociations that are recorded independent of those data objects, but asdynamic associations that are recorded and absorbed by the data objectsaccording to various attributes of those data objects. According to someaspects, a data object has relational awareness score with respect toeach of its associated data object relationships. This allows the dataobject to have an independent recognition of various data objectrelationships, including data object relationships that are typicallymodeled indirect data object relationships in traditional graph models,while being able to distinguish between more significant data objectrelationships (e.g., data object relationships having higher respectiverelational awareness scores) and less significant data objectrelationships (e.g., data object relationships having lower respectiverelational awareness scores).

Various embodiments of the present invention address the notedshortcomings of the traditional graph-based database by introducing datamodels that process relationships between data objects not as staticassociations that are recorded independent of those data objects, but asdynamic associations that are recorded and absorbed by the data objectsaccording to various attributes of those data objects. For example,various embodiments of the present invention enable recording numerousrelationships between data objects as well as associating each recordedrelationship with a dynamically-generated relational awareness scorethat indicates the significance of relationships to associated dataobjects. As discussed in greater detail below, relational awarenessscores for particular data objects may be determined based at least inpart on individual attributes of the particular data objects, individualattributes of hierarchical parent data objects of the particular dataobjects, individual attributes of data objects that are operationallyrelated to the particular data objects, individual attributes of dataobjects that are deemed sufficiently similar to the particular dataobjects, significance of data objects and/or data object relationshipsto environment spaces of a data interaction environment, and/or thelike.

Through utilizing the dynamic relational awareness concepts describedherein, data retrieval and/or data visualization may be rendered moreefficient and effective. Various embodiments of the present inventionintroduce efficient techniques for data retrieval and/or datavisualization that utilize relational awareness scores and/or absorptionscores. Furthermore, utilizing the dynamic relational awareness conceptsdescribed herein that are more scalable and are better capable ofintegrating external data objects into a relationally aware data modelcompared to existing data management solutions. Various embodiments ofthe present invention introduce efficient techniques for efficiently andeffectively integrating external data objects into relationally awaredata models. Through utilizing at least one of the dynamic relationalawareness concepts described herein, the data retrieval conceptsdiscussed herein, the data visualization concepts described, and theexternal integration concepts described herein, various embodiments ofthe present invention address various shortcomings of existing datamanagement solutions (e.g., various shortcomings of existing graph-baseddata management solutions) and make important technical contributions toefficiency and effectiveness of such data management solutions.

In some embodiments, relational awareness score enable a data object tomaintain self-awareness regarding its context. In some embodiments,updating of data objects may cause a change in values that encoderelational properties of the data objects. In this sense, changes indata fields associated with a data object affects the relational contextof the noted data object by, for example, changing relational awarenessscores associated with the noted data object and other data objects. Insome of the noted embodiments, the relational context updatingtechniques introduced herein enable a data object to exerciseself-awareness not only regarding its immediate data context but alsowith respect to its relational context vis-à-vis other data objects. Forexample, the updating of the contents of a document data object causeschanges in the relational scores encoding the strength of relationshipsof the document object with other data objects (e.g., with other taskdata objects).

Data Interaction Platform

FIG. 4 provides an operational example of a user interface 400 for adata interaction platform that may be generated by the data interactionplatform computing entity 106 and that may utilize at least some of thedynamic relational awareness concepts, the data visualization concepts,and the external integration concepts discussed in the present document.The user interface 400 includes user interface elements 401-408 as wellas user interface element 410. The user interface elements 401-408 eachcorrespond to a hierarchical meta-type designation characterizing rootnodes of a hierarchical dependency structure between data objectsutilized by the data interaction platform. As further described below,the data interaction platform maintains a hierarchy of data objects,where each child data object hierarchically depends from one or moreparent data objects. For example, a data object corresponding to aparticular person who is an employee of a particular company and agraduate of a particular university may be a hierarchical dependent of adata object associated with employees of the particular company and adata object associated with graduates of the particular university. Thedata object associated with the employees of the particular company mayin turn be a hierarchical dependent of a data object associated withworking adults, while the data object associated with graduates of theparticular university may in turn be a hierarchical dependent of a dataobject associated with university graduates. As further discussed below,an innovative aspect of the present invention relates to utilizingrelational awareness signals provided at each level of a hierarchicaldependency structure between data objects (e.g., absorption scores ofeach parent data object for a particular data object) to generaterelational awareness scores for particular data objects.

In some embodiments, the hierarchical dependency structure relates eachdata object to at least one of various preconfigured hierarchicalmeta-type designations, where each hierarchical meta-type designationmay relate to foundational properties of the data object that give auniversal meaning to its relationship with other data objects. Asdescribed above, the preconfigured hierarchical meta-type designationsmay server as root nodes of a hierarchical dependency structure betweendata objects utilized by the data interaction platform. Variousapproaches may be adapted to define such preconfigured hierarchicalmeta-type designations, where each approach may utilize differentfoundational properties of data objects to define preconfiguredhierarchical meta-type designations and/or maintain different levels ofgranularity for defining preconfigured hierarchical meta-typedesignations. In the exemplary approach depicted in the user interface400 of FIG. 4, the preconfigured hierarchical meta-type designations aredefined based on primary and potentially secondarycharacteristics/classifications to include a “living” designationassociated with the user interface element 401, a “places” designationassociated with the user interface element 402, a “things” designationassociated with the user interface element 403, a “time” designationassociated with the user interface element 404, an “actions” designationassociated with the user interface element 405, a “communications”designation associated with the user interface element 406, a“groupings” designation associated with the user interface element 407,and a “knowledge” designation associated with the user interface element408. However, a person of ordinary skill in the relevant technology willrecognize that other formulations of the various preconfiguredhierarchical meta-type designations are feasible and may conferparticular advantages in various implementations and use cases.

Depending on system semantics, the “living” hierarchical meta-typedesignation may relate to data objects describing people, contacts,animals, plants, and/or the like. An operational example of a userinterface depicting visual relationships of particular “living” dataobjects that may be generated in response to user selection of userinterface element 401 is presented in FIG. 5. The user interfacedepicted in FIG. 5 includes a visualization of various target “living”data objects, such as the “living” data object corresponding to theindividual named “Pooya Shoghi,” whose visual representation is depictedusing the user interface element 501 in the user interface of FIG. 5. Asdepicted in the user interface of FIG. 6, a user selection of the userinterface element 501 depicts data objects that are related to theselected “living” data object, where the data objects are in turnorganized by the preconfigured hierarchical meta-type designationsdiscussed above in relation to user interface elements 401-408, hereassociated with the user interface elements 601-608 respectively. Theuser interface depicted in FIG. 5 further enables adding new dataobjects that are related to the selected “living” data object byselecting a designation for a new data object via the user interfaceelement 610 and selecting the user interface element 611, which in turnleads to display of the user interface depicted in FIG. 7, whichincludes a form for entering attributes of the new data object (such asa company name attribute name that can be entered using user interfaceelement 701, a company industry sector attribute name that can beentered using user interface element 702, and company address attributesthat can be entered using user interface elements 703).

Depending on system semantics, the “places” hierarchical meta-typedesignation may relate to data objects describing locations, cities,regions, countries, continents, and/or the like. A “places” data objectmay have relationships with data objects of other hierarchical meta-typedesignations. For example, a “places” data object may have a “was bornin” relationship with a “living” data object. As another example, a“places” data object may have a “will be performed in” relationship withan “action” data object. As yet another example, a “places” data objectmay have a “is located in” relationship with a “things” data object. Asa further example, a “places” data object may have “occurred in”relationship with a “communications” data object.

Depending on system semantics, the “things” hierarchical meta-typedesignation may relate to data objects describing buildings, products,inanimate objects, equipment, inventory, and/or the like. A “things”data object may have relationships with data objects of otherhierarchical meta-type designations. For example, a “things” data objectmay have a “purchased” relationship with a “living” data object. Asanother example, a “things” data object may have a “is generated using”relationship with an “action” data object. As yet another example, a“things” data object may have a “is located in” relationship with a“places” data object. As a further example, a “things” data object mayhave “was a subject of” relationship with a “communications” dataobject. In some embodiments, the “things” data objects may be selectedvia files of preconfigured formats which are configured to generatevisualizations of the noted “things” data objects, for example a filethat describe a visualization of a building or a product usingrelational awareness modeling data associated with the building or theproduct. FIG. 12 provides an operational example of a file selectionuser interface that may be generated in response to user selection ofuser interface element 403 in order to enable a user to select a filewith a preconfigured format that describe a visualization of a “things”data object.

Depending on system semantics, a “time” hierarchical meta-typedesignation may relate to data objects describing seconds, minutes,hours, dates, date or time ranges, and/or the like. A “time” data objectmay have relationships with data objects of other hierarchical meta-typedesignations. For example, a “time” data object may have a “was born on”relationship with a “living” data object. As another example, a “time”data object may have a “will be performed on” relationship with an“action” data object. As yet another example, a “time” data object mayhave a “was purchased on” relationship with a “things” data object. As afurther example, a “time” data object may have “occurred on”relationship with a “communications” data object. In some embodiments, atime data object may be a category of particular events. In someembodiments, a time data object may be used in linear and non-linearmanners and may be deemed related to an action data object. A time dataobject may also be used to describe “active” and “inactive” statuses,such as a person being considered “active” during periods that fallwithin their life span and inactive after their period of death.

Time may also be associated with variations of various otherdesignations, like object properties. By example, the start time of anobject may have an associated size, like size a birth or size as aseedling, with said property being modified through course of the Timeperiod. As an example, a tree may have an associated start size as 1 CM,and a state as a seed, with a secondary Time that is “Related” to thefirst time by a Time Period of X days after adequate light and heat, totransform into a tree of a very small size, with another linearlyincreased size to a maximum size occurring on yet another related Timeperiod from the start Time, then a continuation to an approximate lifespan that does not alter (or does) alter the size or other propertiesuntil the end of that Time.

Depending on system semantics, an “actions” hierarchical meta-typedesignation may relate to data objects describing events, tasks,projects, performances, concerts, and/or the like. An “actions” dataobject may have relationships with data objects of other hierarchicalmeta-type designations. For example, an “actions” data object may have a“was performed by” relationship with a “living” data object. As anotherexample, an “actions” data object may have a “will be performed on”relationship with a “time” data object. As yet another example, an“actions” data object may have a “can be performed by” relationship witha “things” data object. As a further example, an “actions” data objectmay have “was processed using” relationship with a “communications” dataobject. In some embodiments, the “actions” hierarchical meta-typedesignation may have two child data objects, a “tasks” child data objectand a “projects” child data object. FIG. 9 provides an operationalexample of a user interface that may be generated in response to userinterface of user interface element 405 associated with the “actions”hierarchical meta-type designation (a second operational example isdepicted in FIG. 39). As depicted in FIG. 9, the depicted user interfaceincludes user interface elements 901-902, which correspond to the“tasks” data object and “projects” data object respectively. As furtherdepicted in the user interface of FIG. 10, selection of the userinterface element 901 associated with the “tasks” data object relates todepicting various target data objects depending from the “tasks” dataobject, including the “Install ViZZ” data object associated with theuser interface element 901. As further depicted in the user interface ofFIG. 11, selection of user interface element 901 data objects that arerelated to the selected “tasks” data object, where the data objects arein turn organized by the preconfigured hierarchical meta-typedesignations discussed above in relation to user interface elements401-408.

Tasks may refer to a single event, like the kicking of a football, or aseries of events, like the playing of a football game, or theconstruction of a building. Therefore, Tasks may have associated childand parent tasks. As an example, the task of driving a car from work tohome may include the task of starting the car, stopping at stop lights,and the like, while also having parent tasks of all actions (Tasks) forwhich the car was involved, or even all the cars of a city, and thelike.

Depending on system semantics, the “communications” hierarchicalmeta-type designation may relate to data objects describing emails,phone calls, text messages, pager messages, meetings, and/or the like.Communications may also include the communicating of visual, audio andother information such as related in photos, videos, x-rays,multi-dimensional interaction such as within 3D models, and the relatingof sensory information, such as smells, etc. A “communications” dataobject may have relationships with data objects of other hierarchicalmeta-type designations. For example, a “communications” data object mayhave a “was received by” relationship with a “living” data object. Asanother example, a “communications” data object may have a “includesguidelines for” relationship with an “action” data object. As yetanother example, a “communications” data object may have a “discussesprice of” relationship with a “things” data object. As a furtherexample, a “communications” data object may have “occurred in”relationship with a “time” data object.

Depending on system semantics, the “groupings” hierarchical meta-typedesignation may relate to data objects describing companies, teams,organizations, email lists, and/or the like. A “groupings” data objectmay have relationships with data objects of other hierarchical meta-typedesignations. For example, a “groupings” data object may have a “is aparticipant in” relationship with a “living” data object. As anotherexample, a “groupings” data object may have a “is expected to perform”relationship with an “action” data object. As yet another example, a“groupings” data object may have a “is owner of” relationship with a“things” data object. As a further example, a “groupings” data objectmay have “was formed in” relationship with a “time” data object. In someembodiments, a groupings data object may signify a relationship betweenthe data objects in each group, for example a collection of people maybe represented by a group data object of a company, thereby creating arelationship, via that company, of those contacts.

Depending on system semantics, the “knowledge” hierarchical meta-typedesignation may relate to data objects describing files, documents,books, articles, and/or the like. A “knowledge” data object may haverelationships with data objects of other hierarchical meta-typedesignations. For example, a “knowledge” data object may have a “isauthored by” relationship with a “living” data object. As anotherexample, a “knowledge” data object may have a “describes how to perform”relationship with an “action” data object. As yet another example, a“knowledge” data object may have a “includes information about”relationship with a “things” data object. As a further example, a“knowledge” data object may have “was authored in” relationship with a“time” data object. In some embodiments, the “knowledge” hierarchicalmeta-type designation may have two child data objects, a “files” childdata object and a “documents” child data object. FIG. 11 provides anoperational example of a user interface that may be generated inresponse to user interface of user interface element 408 associated withthe “knowledge” hierarchical meta-type designation. As depicted in FIG.11, the depicted user interface includes user interface elements1101-1102, which correspond to the “files” data object and the“documents” data object respectively. A knowledge data object may alsohave “is related to” information within the same characteristic class ofknowledge items to other information on the same subject matter.

In some embodiments, a data object may be simultaneously associated withone or more hierarchical meta-type designations and/or may be acollection of one or more data objects of different hierarchicalmeta-type designations. For example, a person object may be a collectionof interactions, living elements, relationships to others, etc. Asanother example, a photo object may be a collection of the subjectmatter, when it was taken, where it was taken, who took it, etc. As yetanother example, an event data object may be a collection of the peopleor objects related to that event, the actions that occurred during theevent, where the event took place, when the event took place, etc. Insome embodiments, data becomes both more enriched, more self-aware, moreuseable, more storage-wise efficient, and more accessible (easily foundby its identity), thus eliminating the need for utilizing nominaldesignators like file names and improving the ability to find thingsbased on their unique characteristics. In some embodiments, thecharacteristics of an object can be temporally dynamically, e.g., suchcharacteristics can change through time and experience, thus a dataobject for a person entity may change from childhood to adulthood.

Returning to FIG. 4, the user interface 400 further includes the userinterface element 410 which enables user selection of one or moreenvironment states for the data interaction platform. An environmentstate of a data interaction platform may indicate an inferred userpurpose and/or an indicated user purpose behind usage of the datainteraction platform at a particular time. Environment states may begenerated based at least in part on user-supplied information and/or byperforming machine learning analysis of the usages of data at differenttime intervals and/or in different locations. For example, the datainteraction platform computing entity 106 may infer based at least inpart on user interaction data that the user uses separate groups of dataobjects at different time intervals and thus conclude that the separategroups of data objects belong to different environments. Moreover,selection of one or more environment states for a particular usagesession may be performed based at least in part on explicit userselection and/or based at least in part on detecting that the user is ata time-of-day and/or at a location associated with a particularenvironment state. For example, the data interaction platform computingentity 106 may infer a “working” environment state for a particularusage session by a user during working hours and/or while the user islocated at a geographic location of the user's office. As furtherdiscussed below, an innovative aspect of the present invention relatesto utilizing relational awareness signals provided by the environmentstates for usage of a data interaction platform to generate relationalawareness scores for particular data objects.

FIG. 13 provides an operational example of a user interface that enablesuser selection of environment states. As depicted in the user interfaceof FIG. 13, the defined environment states are divided into fourmeta-type designations: a “live” environment 1301 (e.g., related toprivate or personal environment states) that includes the environmentstate “Pooya's Private Workspace” 1311, a “work” environment 1302 (e.g.,related to professional environment states), a “play” environment 1303(e.g., related to entertainment-related or leisure-related environmentstates) that includes the environment state “Pooya's Fun” 1313, and a“global” environment 1304 (e.g., related to general or publicenvironment states) that includes the environment state “Global Public”1314. A user can select an environment state by selecting one or more ofthe appropriate environments. Selection or deselection of environmentstates can affect visualizations of retrieved data item. For example, asdepicted in the user interface of FIG. 14 relative to the user interfaceof FIG. 5, after selection of the environment state “Global Public”1314, selection of the user interface element 401 leads to generationand display of a more crowded visualization with a greater number ofdepicted data objects compared to prior to selection of the environmentstate “Global Public” 1314. In some embodiments, environments can beutilized to define security parameters for accessing particular dataobjects and/or particular inter-object relationships. Users may createor select more than one environment at a time. By example, a user mayhave more than one job, and thus more than one work environment. Thiscan include past jobs with differing states (Active, Inactive,Temporary, etc), or multiple simultaneous jobs. Users may also choose todisplay more than one simultaneous state, such as work and home data.Data visualization can also be impacted by user preference, throughdiscernment using AI or other logic to determine most relevant data, byuser geographic location, time of day, etc, to alter display ofinformation and visual relevance of information. By example, though auser may have both work and home environments open, because they are atwork and are engaged in work activities, the work-related informationmay be given visual preference over the home information. Further,because the user is accessing specific data, such as the projects theyare working on, the information display for those projects may also begiven priority over other project information, as may informationrelative to what they are currently doing, the people within thedepartment they work in or who are also working on the same projects,and the like. In some embodiments, the data objects accessible by anend-user may be determined by one or more security policies associatedwith the data object.

The term “security environment” may refer to data that indicate one ormore runtime parameter value ranges for one or more runtime parametersassociated with a data access session by a user profile which may affectthe ability of the user profile to access particular data objects. Forexample, a particular security environment may be defined by at leastone of a location-based runtime parameter value indicating a particulargeographic area (e.g., a particular geographic area corresponding to aparticular office of a particular company), a temporal runtime parametervalue indicating a particular range of time within a week (e.g., everyweekday between 9 AM and 5 PM), a network connection-based runtimeparameter indicating a particular network connection used to connect tothe data interaction platform computing entity (e.g., a particularvirtual private network (VPN) associated with a company), anenvironment-selection runtime parameter indicating an environment stateselected by a user of the data interaction platform computing entity 106(e.g., an environment state associated with work or leisure), ajurisdictional runtime parameter indicating a legal and/or regulatoryjurisdiction of a user profile associated with the data access request,and/or the like.

The example data interaction platform depicted and described hereinusing FIGS. 4-14 can be utilized to process data retrieval queries andgenerate responsive query outputs, where a data retrieval query is anyrequest to retrieve one or more data objects that correspond toparticular data retrieval query criteria, e.g., one or more filteringcriteria, one or more search criteria, and/or the like. For example,FIG. 15 provides an operational example of a user interface 1500 forprocessing data retrieval queries using the noted data interactionplatform (a second operational example is presented in FIG. 38). Asdepicted in FIG. 15, the user interface 1500 includes user interfaceelements 1501 for specifying data retrieval query criteria, userinterface elements 1502 for specifying visualization parameters defininga desired visualization of data, and user interface elements 1503depicting search results. As further depicted in the user interface 1600of FIG. 16, query outputs can be saved as sessions 1601-1602 andvisualization results 1603 may include relationships between retrieveddata objects. Processing data retrieval queries using a proposed datainteraction platform will be described in greater detail below.

To provide the data modeling, data visualization, external integration,and query processing functionalities discussed herein, a datainteraction platform utilizing dynamic relational awareness needs toutilize a robust logical data model that enables both relationalawareness modeling aspects as well as dynamic user interaction aspectsof the noted functionalities. An example of such a logical data model1700 for a data interaction system is provided in FIG. 17. As depictedin FIG. 17, a user node 1701 is associated with a user profile object1702, which uniquely identifies the user node 1701 within the datainteraction platform, encodes attributes and relationships of the usernode 1701 in relation to the data interaction platform, and enables theuser node 1701 to interact with other user nodes 1701 within the datainteraction platform. The user profile object 1702 manages various dataobjects, such as a collaboration space 1703 of user profile objectswhose access to the data interaction platform is controlled by the usernode 1701, a shared space 1704 of data objects that were shared by theuser node 1701 with other user profile objects within the datainteraction platform and which may include primary data objects such asprimary data object 1741 or other shared spaces such as shared space1742, a team object 1705 that enables the user node 1701 to manageaccess to its data on a group level, environment objects 1706 eachidentifying an environment state associated with the user node 1701, andenvironment classes 1707 each identifying a meta-type designation ofenvironment states associated with the user node 1701.

As further depicted in the logical data model 1700 of FIG. 17, userprofile object 1702 owns a space object 1708 which may act as containerof multiple data objects and which may include one or more space objectssuch as space object 1781, one or more primary data objects such asprimary data object 1782, and one or more secondary data objects such assecondary data object 1783. Moreover, user profile object 1702 owns aprimary data object 1709 which may act as a primary data node and whichmay include one or more space objects such as space object 1791, one ormore primary data objects such as primary data object 1792, and one ormore secondary data objects such as secondary data object 1794. In someembodiments, a secondary data object is a data object that is defined byassociation with another data object such that it will be deleted upondeletion of the other data object. An example of a secondary data objectis a phone number data object for an individual person data object. Insome embodiments, at least some of the data objects depicted in thelogical data model 1700 of FIG. 17 are “default” data objects, meaningthat they are automatically created upon creation of a user profileobject. In some embodiments, the default data objects include one ormore of the team object 1705, the collaborator space 1703, and theshared space 1704.

Data Modeling and Data Retrieval

FIG. 18 is a flowchart diagram of an example process 1800 for generatingrelational awareness models for a data interaction platform. Via thevarious steps/operations of the process 1800, the relational awarenessmodeling engine 111 of the data interaction platform computing entity106 can generate relational awareness scores for data objectrelationships with respect to related data objects in an efficient andeffective manner.

The process 1800 begins at step/operation 1801 when the relationalawareness modeling engine 111 generates an absorption score for eachdata object associated with a data interaction platform. In someembodiments, the absorption score for a data object indicates anestimated relational awareness tendency of the data object. Differentdata objects may exhibit different expected capacities of formingrelationships with other data objects. For example, a “living” dataobject may typically have a stronger tendency to be aware of itsrelationships relative to a “communications” data object. The notedexample illustrates both the diverse absorption scores of various dataobjects as well as the role of hierarchical relationships in forming thediverse absorption scores of various data objects. As described below,hierarchical relationships are one of many inferential signals that therelational awareness modeling engine 111 can use to infer absorptionscores for various data objects.

In some embodiments, step/operation 1801 can be performed in accordancewith the process depicted in FIG. 19, which is a flowchart diagram of anexample process for generating an absorption score for a particular dataobject. The process depicted in FIG. 19 begins at step/operation 1901when the relational awareness modeling engine 111 generates anindividual absorption score for the particular data object. In someembodiments, the individual absorption score of the particular dataobject indicates an estimated relational awareness tendency of theparticular data object given one or more individual attributes of theparticular data object. For example, based at least in part on anexample model for inferring individual absorption scores, a data objectassociated with a particular individual person having a high educationaldegree may be deemed to have a high absorption score. As anotherexample, based at least in part on another example model for generatingindividual absorption scores, a data object a data object associatedwith a particular individual person having a particular physical profile(e.g., age, height, weight, and/or the like) may be deemed to have ahigh absorption score.

In some embodiments, step/operation 1901 can be performed in accordancewith the process depicted in FIG. 20, which is a flowchart diagram of anexample process for generating an individual absorption score for aparticular data object. The process depicted in FIG. 20 begins atstep/operation 2001 when the relational awareness modeling engine 111obtains one or more individual attributes for the particular dataobject. Examples of individual attributes for the particular data objectinclude dynamic attributes generated in accordance with the data schemacode segment 2100 of FIG. 21, dynamic properties generated in accordancewith the data schema code segment 2200 of FIG. 22, and the staticproperties generated in accordance with the data schema code segment2300 of FIG. 23.

At step/operation 2002, the relational awareness modeling engine 111generates one or more absorption metrics based at least in part on theone or more individual attributes obtained in step/operation 2001. Insome embodiments, an absorption metric of a particular data object is aproperty of the particular data object that is determined based at leastin part on the individual attributes for the particular object and thatcan be used to estimate the individual absorption score of theparticular data object. In some embodiments, generating the one or moreabsorption metrics based at least in part on the one or more individualattributes includes selecting a subset of the individual attributesbased at least in part on an input space of an individualized absorptionspace configured to generate individual absorption scores for dataobjects based at least in part on absorption metrics for the dataobjects. In some embodiments, generating the one or more absorptionmetrics based at least in part on the one or more individual attributesincludes performing a dimensionality reduction and/or feature embeddingalgorithm on the one or more individual attributes.

In some embodiments, data objects may be configured to explore andidentify relationship properties between themselves and other dataobjects. For example, a document object may be configured to explore toidentify document objects of similar subject, and then createrelationships between itself and the identified documents and generate arelational awareness score for each created relationship based on bothsimilarity and relevance of the subject matters of the two documentobjects. In some embodiments, the self-exploration concepts discussedherein enable a data model to be organically self-building, whichreduces and/or eliminates the need for expensive data modelingoperations.

At step/operation 2003, the relational awareness modeling engine 111maps the one or more absorption metrics generated in step/operation 2002to an individual absorption space. An individual absorption space may bea space configured to relate absorption metrics for various data objectsthat include a first set of data objects with known individualabsorption scores and a second set of data objects with unknownindividual absorption scores. An operational example of an individualabsorption space 2400 is depicted in FIG. 24. As depicted in FIG. 24,the individual absorption space 2400 includes input dimensions 2401-2403associated with absorption metrics A-C respectively. Each point2411-2416 depicts a corresponding data object. As further depicted inthe individual absorption space 2400 of FIG. 24, points having solidboundaries (e.g., points 2411 and 2414) have known individual absorptionscores, while points having dashed boundaries (e.g., points 2412-2413and 2415-2416) have unknown individual absorption scores.

At step/operation 2004, the relational awareness modeling engine 111generates the individual absorption score based at least in part on theindividual absorption space. In some embodiments, to generate theindividual absorption score for a particular data object, the relationalawareness modeling engine 111 generates, based at least in part on theindividual absorption space, one or more clusters of data objectsincluding a cluster that includes the point corresponding to absorptionmetrics for the particular data object as mapped to the individualabsorption space as well as points corresponding to one or more dataobjects with known individual absorption spaces. In some embodiments, togenerate the individual absorption score for a particular data object,the relational awareness modeling engine 111 finds the closest K dataobjects with known individual absorption spaces whose correspondingpoints in the individual absorption space is closest to the pointcorresponding to the absorption metrics for the particular data objectand generates the individual absorption score for the particular dataobject based at least in part on the known individual absorption spacesfor the closest K data objects, where K may be a preconfiguredhyper-parameter of the relational awareness modeling engine 111 and mayhave a value of one or more.

An operational example of generating individual absorption scores basedat least in part on the individual absorption space is depicted in theindividual absorption space 2400 of FIG. 24. As depicted in FIG. 24, theindividual absorption space 2400 includes two object clusters: objectcluster 2421 and object cluster 2422. In accordance with the clusteringarrangement depicted in FIG. 24, the relational awareness modelingengine 111 may utilize the known individual absorption score for thedata object corresponding to the point 2411 to generate individualabsorption scores of the data objects corresponding to the points2412-2413. Moreover, further in accordance with the clusteringarrangement depicted in FIG. 24, the relational awareness modelingengine 111 may utilize the known individual absorption score for thedata object corresponding to the point 2414 to generate individualabsorption scores of the data objects corresponding to the points2415-2416.

Returning to FIG. 19, at step/operation 1902, the relational awarenessmodeling engine 111 generates a hierarchical absorption score for theparticular data object. For example, the hierarchical absorption scorefor a particular data object that has a hierarchical parents P1, P2, andP3 may be determined based at least in part on individual absorptionscores of P1, P2, and P3. In some embodiments, the hierarchicalabsorption score for the data object is determined based at least inpart on each individual absorption score for a parent data object thatis a hierarchical parent of the data object. In some embodiments, theone or more parent data objects for a particular data object include ahierarchical meta-type of the particular data object, where thehierarchical meta-type of the particular data object indicates whetherthe particular data object is comprising one or more relatedhierarchical meta-type designations of a plurality of predefinedhierarchical meta-type designations. In some embodiments, the pluralityof predefined hierarchical meta-type designations include: a firstpredefined hierarchical meta-type designation associated with livingreal-world entities, a second predefined hierarchical meta-typedesignation associated with non-living-object real-world entities, athird predefined hierarchical meta-type designation associated withlocation-defining real-world entities, a fourth predefined hierarchicalmeta-type designation associated with time-defining real-world entities,a fifth predefined hierarchical meta-type designation associated withcommunication-defining entities, a sixth predefined hierarchicalmeta-type designation associated with group-defining entities, and aseventh predefined hierarchical meta-type designation associated withknowledge-defining entities.

At step/operation 1903, the relational awareness modeling engine 111generates an operational absorption score for the particular dataobject. In some embodiments, the operational absorption score for thedata object is determined based at least in part on each individualabsorption score for a related data object that is operationally relatedto the particular data object. In some embodiments, a related dataobject is deemed related to a particular data object if there is anon-hierarchical relationship between the two data objects. In someembodiments, the one or more related data objects for a particular dataobject of include one or more user-defining objects associated with theparticular data object and one or more access-defining data objectsassociated with the particular data object. In some embodiments, the oneor more user-defining objects associated with the particular data objectinclude one or more primary user-defining objects associated with theparticular data object and one or more collaborator user-definingobjects associated with the particular data object. In some embodiments,the one or more access-defining data objects associated with theparticular data object include one or more sharing space data objectsassociated with the particular data object (e.g., a public sharing spacedata object, a collaborator space object, a shared space object, and/orthe like).

At step/operation 1904, the relational awareness modeling engine 111generates an attribute-based absorption score for the particular dataobject. In some embodiments, the attribute-based absorption score forthe particular data object is performed based at least in part on eachindividual absorption score for a similar data object whose respectiveindividual attributes are determined to be sufficiently similar to theone or more object attributes of the particular data object. In someembodiments, the relational awareness modeling engine 111 generates adistance measure between each pair of data objects and determinesparticular pairs of data objects whose distance measure exceeds athreshold distance measure. In some of those embodiments, the relationalawareness modeling engine 111 generates an attribute-based absorptionscore for a particular data object based at least in part on any dataobject that is member of a particular pair of data objects that alsoincludes the particular data object.

At operation 1905, the relational awareness modeling engine 111generates the absorption score for the particular data object based atleast in part on the individual absorption score for the particular dataobject, the hierarchical absorption score for the particular dataobject, the operational absorption score for the particular data object,and the attribute absorption score for the particular data object. Insome embodiments, to generate the absorption score for the particulardata object, the relational awareness modeling engine 111 applies aparameter to each of the individual absorption score for the particulardata object, the hierarchical absorption score for the particular dataobject, the operational absorption score for the particular data object,and the attribute absorption score for the particular data object, whereeach parameter may be determined using a preconfigured absorption scoregeneration model such as a generalized linear model and/or using asupervised machine learning algorithm for determining absorption scores.

Returning to FIG. 18, at step/operation 1802, the relational awarenessmodeling engine 111 generates an environment-based absorption score foreach data object relationship. In some embodiments, theenvironment-based absorption score of a particular data objectrelationship indicates an estimated relational significance of theparticular data object relationship given an environment state of thedata interaction platform. In some embodiments, the environment state ofthe data interaction platform is selected from a plurality of candidateenvironment states of the data interaction platform. In some of thoseembodiments, the plurality of candidate environment states of the datainteraction platform indicates at least one of the following: one ormore private environment states, one or more professional environmentstates, one or more leisure environment state, and one or more publicenvironment states.

At step/operation 1803, the relational awareness modeling engine 111generates relational awareness scores based at least in part on theabsorption scores for data objects determined in step/operation 1801 andenvironment-based absorption scores for data object relationshipsdetermined in step/operation 1803. In some embodiments, for each dataobject relationship of the plurality of data object relationships thatis associated with a plurality of related data objects, relationalawareness modeling engine 111 generates a relational awareness scorewith respect to each of the plurality of related data objects associatedwith the data object relationship. In some of those embodiments, therelational awareness score for the data object relationship with respectto a particular related data object of the plurality of related dataobjects is determined based at least in part on the absorption score ofthe particular related data object and the environment-based absorptionscore for the data object relationship. In some embodiments, to generaterelational awareness scores, the relational awareness modeling engine111 applies a parameter to each of the absorption scores for the dataobjects and environment-based absorption scores for the data objectrelationships, where each parameter may be determined using apreconfigured relational score generation model such as a generalizedlinear model and/or using a supervised machine learning algorithm fordetermining relational awareness scores.

In some embodiments, the query processing engine 112 of the datainteraction platform computing entity 106 uses the relational awarenessscores generated by the relational awareness modeling engine 111 atstep/operation 1803 to process one or more data retrieval queries. Insome of those embodiments, to process a data retrieval query, the queryprocessing engine 112 identifies one or more filtering parametersassociated with the data retrieval query, generates a query relevancescore for each data object relationship based at least in part on one ormore filtering parameters and each relational awareness score associatedwith the data object relationship, and generates a query output based atleast in part on each query relevance score for a data objectrelationship.

In some embodiments, a relational score may have multiple scorecomponents, such as a negative score component and a positive scorecomponent. In some of the embodiments where a relational score has apositive component a negative component, the score components are viewedas independent from one another and not summed up in a cumulativemanner. For example, a relationship may be associated with a positivescore component of ten and a negative score component of ten, but thetwo values are not summed up to generate a value of zero. In someembodiments, relational scores may be displayed graphically usingtextual representations and/or using graphical representations, forexample by utilizing one or more of the data visualization techniquesdescribed herein (including one or more multi-dimensional datavisualization techniques described herein).

In some embodiments, when relational awareness scores change over time,past scores may be maintained and be accessible to generate time-seriesdata that reflect changes in relational scores over time. For example, arelational awareness score between two people may start off at a 3,increase over time to an 8, and then decrease to 1. The relationalawareness modeling engine 111 may be configured to analyze suchfluctuations to generate one or more interaction recommendations betweenthe noted two people. Moreover, various events may be associated withvarious relational impact scores. For example, an event may have anegative but temporary impact score of −9.

Data Visualization

FIG. 25 is a flowchart diagram of an example process 2500 for generatinga visual representation of a group of retrieved data objects which havebeen retrieved in response to a data retrieval query. Using the varioussteps/operations of process 2500, the data visualization engine 113 ofthe data interaction platform computing entity 106 can performvisualization of data objects using relational awareness metrics.

The process 2500 begins at step/operation 2501 when the datavisualization engine 113 generates one or more absorption parameters forthe retrieved data object. In some embodiments, an absorption parameterfor the retrieved data object is any parameter used to determine anabsorption parameter for the retrieved data object. Examples ofabsorption parameters include one or more of individual absorptionparameters, hierarchical absorption parameters, operational absorptionparameters, attribute-based absorption parameters, environment-basedabsorption parameters, and/or the like. In some embodiments, the one ormore absorption parameters for a particular retrieved data object mayinclude an individual absorption score for the particular retrieved dataobject, and the individual absorption score of the particular retrieveddata object indicates an estimated relational awareness capacity of theparticular retrieved data object given one or more object attributes ofthe particular retrieved data object. In some embodiments, the one ormore absorption parameters for a particular retrieved data objectinclude a hierarchical absorption score for the particular retrieveddata object, and the hierarchical absorption score of the particularretrieved data object is determined based at least in part on eachindividual absorption score for a parent data object that is ahierarchical parent of the particular retrieved data object. In someembodiments, the one or more absorption parameters for a particularretrieved data object include an operational absorption score for theparticular retrieved data object, and the operational absorption scoreof the particular retrieved data object is determined based at least inpart on each individual absorption score for a related data object thatis operationally related to the particular retrieved data object. Insome embodiments, the one or more absorption parameters for a particularretrieved data object include an environment-based absorption score forthe particular retrieved data object, and the environment-basedabsorption score of the particular retrieved data object indicates anestimated relational significance of the particular retrieved dataobject to an environment state of a data interaction platform executingthe data retrieval query.

At step/operation 2502, the data visualization engine 113 generates oneor more visualization configuration parameters for each retrieved dataobject based at least in part on the one or more absorption parametersfor the retrieved data object generated in step/operation 2501. In someembodiments, a visualization configuration parameter for a particularretrieved data object indicates at least one visual feature of an iconassociated with the particular retrieved data object. In someembodiments, the one or more visualization configuration parameters fora particular retrieved data object include one or more visualizationlocation coordinates for the particular retrieved data object, one ormore shape-defining visualization configuration parameters for theparticular retrieved data object, one or more rotation-speed-definingvisualization configuration parameters for the particular retrieved dataobject, one or more color-defining visualization configurationparameters for the particular retrieved data object, one or morelighting/highlight visualization configurations configurationparameters, and one or more pulse-intensity-defining visualizationconfiguration parameters for the particular retrieved data object. Insome embodiments, to generate the visualization configuration parametersfor the retrieved data object, the data visualization engine 113 mapsthe one or more absorption parameters for the retrieved data object ofthe plurality of retrieved data objects to a visualization spacecomprising one or more input dimensions associated with the one or moreabsorption parameters and one or more output dimensions associated withone or more visualization configuration parameters and generates thevisualization configuration parameters based at least in part on thevisualization space. In some embodiments, at least some of thevisualization configuration parameters are determined based at least inpart on user input, e.g., user input entered using a visualization toolsselection user interface such as the example visualization toolsselection user interface 3400 of FIG. 34.

In some embodiments, relational awareness modeling engine 111 tracksvariations in relational awareness scores. In some embodiments,relational awareness modeling engine 111 tracks relative volatility ofrelational awareness scores for a particular data object over time. Insome embodiments, the relational awareness modeling engine 111 uses thevariations in relational awareness scores and/or relative volatility ofrelational awareness scores for a particular data object over time togenerate predictive conclusions about the behavior of the data object.For example, substantial fluctuations in relational awareness scores ofa person data object may indicate psychological instability. As anotherexample, substantial fluctuations in relational awareness scores of anorganization data object may indicate poor management.

In some embodiments, step/operation 2502 may be performed in accordancewith the process depicted in FIG. 26, which is a flowchart diagram of anexample process for generating visualization configuration parametersfor a particular retrieved data object. The process depicted in FIG. 26begins at step/operation 2601 when the data visualization engine 113generates one or more visualization location coordinates for theparticular retrieved data object. In some embodiments, each of the oneor more visualization location coordinates for the particular retrieveddata object correspond to a dimension of an n-dimensional visualizationspace associated with a data interaction platform. In some embodiments,the visualization location coordinates include at least one of thefollowing: a first visualization location coordinate determined based atleast in part on each particular relational awareness score for anyretrieved data object relationship between the particular retrievedparticular data object and other retrieved data objects, a secondvisualization location coordinate determined based at least in part oneach environment-based absorption score for any retrieved data objectrelationship between the particular retrieved data object and the otherretrieved data objects; and a third visualization location coordinatedetermined based at least in part on a hierarchical meta-type of theparticular retrieved data object, where the hierarchical meta-type ofthe particular data object may indicate whether the particular dataobject is comprising one or more related hierarchical meta-typedesignations of a plurality of predefined hierarchical meta-typedesignations.

At step/operation 2602, the data visualization engine 113 generates oneor more shape-defining visualization configuration parameters for theparticular retrieved data object. A shape-defining visualizationconfiguration parameter for the particular retrieved data object maydefine an aspect of a shape of the icon associated with the particularretrieved data object. For example, a particular shape-definingvisualization configuration parameter may define whether the icon for acorresponding retrieved data object is a rectangle, a circle, acylinder, and/or the like. As another example, a particularshape-defining visualization configuration parameter may define size ofthe icon for a corresponding retrieved data object. As yet anotherexample, a particular shape-defining visualization configurationparameter may define size of an aura region associated with the icon fora corresponding retrieved data object. In some embodiments, at least oneshape-defining visualization configuration parameter for a particularretrieved data object may be determined based at least in part on userinput, e.g., user input defining that an aspect of shape of an iconassociated with the particular retrieved data object should indicate aparticular attribute of the particular retrieved data object. Forexample, as depicted in the example visualization configuration userinterface 2700 of FIG. 27, the current state of the user interfaceelement 2701 enables a user to depict an indication of a middle name ofa “living” data object based at least in part on an aura of the icon ofthe “living” data object, e.g., such that an icon for a “living” dataobject having a middle name will have a larger aura and/or such that anicon for a “living” data object having a middle name whose first letterhas alphabetic precedence over other “living” data objects has a largeraura.

At step/operation 2603, the data visualization engine 113 generates oneor more color-defining visualization configuration parameters for theparticular retrieved data object. A color-defining visualizationconfiguration parameter for the particular retrieved data object maydefine an aspect of a color of the icon associated with the particularretrieved data object. For example, a particular color-definingvisualization configuration parameter may define whether the icon for acorresponding retrieved data object is red, blue, yellow, and/or thelike. As another example, a particular color-defining visualizationconfiguration parameter may define a color of a spectral region of anicon associated with the corresponding retrieved data object. In someembodiments, at least one color-defining visualization configurationparameter for a particular retrieved data object may be determined basedat least in part on user input, e.g., user input defining that an aspectof color of an icon associated with the particular retrieved data objectshould indicate a particular attribute of the particular retrieved dataobject. For example, as depicted in the example visualizationconfiguration user interface 2700 of FIG. 7, the current state of theuser interface element 2702 enables a user to depict an indication of anemployer name of a “living” data object based at least in part on aspectral color of the icon of the “living” data object, e.g., such thatan icon for a “living” data object having an employer name will have abrighter spectral color and/or such that an icon for a “living” dataobject having an employer name whose first letter has alphabeticprecedence over other “living” data objects has a brighter spectralcolor.

At step/operation 2604, the data visualization engine 113 generates oneor more rotation-speed-defining visualization configuration parametersfor the particular retrieved data object. A rotation-speed-definingvisualization configuration parameter for the particular retrieved dataobject may define an aspect of a rotation speed of the icon associatedwith the particular retrieved data object. For example, a particularrotation-speed-defining visualization configuration parameter may definewhether the icon for a corresponding retrieved data object rotatesfaster or slow. As another example, a particular rotation-speed-definingvisualization configuration parameter may define whether an iconassociated with the corresponding retrieved data object rotates at all.In some embodiments, at least one rotation-speed-defining visualizationconfiguration parameter for a particular retrieved data object may bedetermined based at least in part on user input, e.g., user inputdefining that an aspect of rotation speed of an icon associated with theparticular retrieved data object should indicate a particular attributeof the particular retrieved data object. For example, as depicted in theexample visualization configuration user interface 2700 of FIG. 7, thecurrent state of the user interface element 2703 enables a user todepict an indication of a first name of a “living” data object based atleast in part on a rotation speed of the icon of the “living” dataobject, e.g., such that an icon for a “living” data object having afirst name whose first letter has alphabetic precedence over other“living” data objects has a slower rotation speed.

At step/operation 2605, the data visualization engine 113 generates oneor more lighting/highlight visualization configuration parameters forthe particular retrieved data object. A lighting/visualizationvisualization configuration parameter for the particular retrieved dataobject may define an aspect of lighting and/or highlight of the iconassociated with the particular retrieved data object. For example, aparticular lighting/highlight visualization configuration parameter maydefine whether the icon for a corresponding retrieved data object islight or dark. As another example, a particular lighting/highlightvisualization configuration parameter may define a discrete and/orcontinuous value for an aspect of lighting and/or highlight of the iconassociated with the particular retrieved data object based on one ormore properties of the retrieved data objects, e.g., one or morerelational awareness scores for relationships associated with theretrieved data object.

At step/operation 2605, the data visualization engine 113 generates oneor more pulse-intensity-defining visualization configuration parametersfor the particular retrieved data object. A pulse-intensity-definingvisualization configuration parameter for the particular retrieved dataobject may define an aspect of a pulse intensity of the icon associatedwith the particular retrieved data object. For example, a particularpulse-intensity-defining visualization configuration parameter maydefine whether the icon for a corresponding retrieved data object pulses(e.g., contracts and expands). As another example, a particularpulse-intensity-defining visualization configuration parameter maydefine whether an icon associated with the corresponding retrieved dataobject pulses fast or slow. As yet another example, a particularpulse-intensity-defining visualization configuration parameter maydefine whether an icon associated with the corresponding retrieved dataobject has a smaller or larger range of contraction and expansion. Insome embodiments, at least one pulse-intensity-defining visualizationconfiguration parameter for a particular retrieved data object may bedetermined based at least in part on user input, e.g., user inputdefining that an aspect of pulse intensity of an icon associated withthe particular retrieved data object should indicate a particularattribute of the particular retrieved data object. For example, thecurrent state of a user interface element may enable a user to depict anindication of a first name of a “living” data object based at least inpart on a pulse intensity of the icon of the “living” data object, e.g.,such that an icon for a “living” data object having a first name whosefirst letter has alphabetic precedence over other “living” data objectspulses slower.

Returning to FIG. 25, at step/operation 2502, the data visualizationengine 113 generates one or more layout configuration parameters for thegroup of retrieved data objects. In some embodiments, a layoutconfiguration parameter defines an aspect of layout of icons associatedwith the group of retrieved data objects. In some of the embodimentsthat utilize layout configuration parameters to define a layout of theicons associated with the group of retrieved data objects, one or morevisualization location coordinates include one or more layout-basedconfiguration parameters that define a location of the particularretrieved data object with respect to the defined layout. In someembodiments, layout configuration parameters are generated based atleast in part on attributes of data object relationships between thegroup of retrieved data objects. In some embodiments, layoutconfiguration parameters are generated based at least in part on userinput, e.g., user input entered using the example display styleselection user interface element 2800 of FIG. 28. Examples of layoutsdefined by layout configuration parameters include cloud layouts (e.g.,the example cloud layout depicted using the user interface 2900 of FIG.29), spiral layouts (e.g., the example spiral layout depicted using theuser interface 3000 of FIG. 30), grid layouts (e.g., the example gridlayout depicted using the user interface 3100 of FIG. 31), line layouts(the example line layout depicted using the user interface 3200 of FIG.32), and cube layouts (the example cube layout depicted using the userinterface 3300 of FIG. 33). Other examples of layouts defined by layoutconfiguration parameters include hierarchical layouts, such as familytrees, organizational charts, workflows, etc.

In some embodiments, the data visualization engine 113 generates thevisual representation based at least in part on each one or morevisualization configuration parameters for a retrieved data object ofthe group of retrieve data objects. An operational example of an exampledata visualization user interface 3500 is presented in FIG. 35. In someembodiments, the data visualization engine 113 generates the visualrepresentation based at least in part on one or more visualizationconfiguration parameters for each retrieved data object relationshipbetween the group of retrieved data objects. In some embodiments, togenerate visualization configuration parameters for a retrieved dataobject relationship, the data visualization engine 113 uses eachrelational awareness score associated with the retrieved data objectrelationship with respect to at least one retrieved data object of thegroup of retrieved data objects. In some embodiments, to generatevisualization configuration parameters for a retrieved data objectrelationship, the data visualization engine 113 uses eachenvironment-based absorption score associated with the retrieved dataobject relationship with respect to an environment state of the datainteraction platform being utilized. In some embodiments, visualizationconfiguration parameters for a retrieved data object relationship mayindicate one or more of tabular data display, multi-line display of text(e.g., a rolodex-style display), radial display (e.g., aFerris-wheel-style display), flash-based display of object-relatedinformation, text visualization properties such as font changes, etc.

External Object Integration

FIG. 36 depicts a flowchart diagram of an example process 3600 forintegrating an external data object into a data model with dynamicrelational awareness. Via the various steps/operations of process 3600,the external integration engine 114 of the data interaction platformcomputing entity 106 can enable external integration of unintegrateddata objects into a data interaction platform comprising a group ofintegrated data object relationships between a group of integrated dataobjects.

The process 3600 begins at step/operation 3601 when the externalintegration engine 114 generates object integration parameters for theexternal data object. In some embodiments, an object integrationparameter for the external data object is an attribute of the externaldata object that can be used to infer data object relationship betweenthe external data object and at least some of the group of integrateddata object relationships. In some embodiments, the one or more objectintegration parameters for the external data object include one or moreentity-type-defining parameters for the external data object, such as anentity-type-defining parameter indicating that the external data objectrelates to a “contacts” data object and/or an entity-type-definingparameter indicating that the external data object relates to a“projects” data object. In some embodiments, the one or more objectintegration parameters for the external data object include one or moretopic-defining parameters for the external data object, such as atopic-defining parameter indicating that the external data objectrelates to business and/or a topic-defining parameter indicating thatthe external data object relates to sports. In some embodiments, the oneor more object integration parameters are generated using an onlinelearning model configured to process user interaction data with theexternal data object outside of the data interaction platform togenerate the one or more object integration parameters. In someembodiments, the online learning model is afollow-the-regularized-leader model.

In some embodiments, the one or more object integration parameters forthe external data object include an individual absorption score for theexternal data object, where the individual absorption score of theexternal data object may indicate an estimated relational awarenesscapacity of the external data object given one or more object attributesof the external data object. In some embodiments, the one or more objectintegration parameters for the external data object include ahierarchical absorption score for the external data object, where thehierarchical absorption score of the external data object may bedetermined based at least in part on each individual absorption scorefor a parent data object that is a hierarchical parent of the externaldata object. In some embodiments, the one or more object integrationparameters for the external data object include an operationalabsorption score for the external data object, where the operationalabsorption score of the external data object may be determined based atleast in part on each individual absorption score for a related dataobject that is operationally related to the external data object. Insome embodiments, the one or more object integration parameters for theexternal data object include an environment-based absorption score forthe external data object, where the environment-based absorption scoreof the external data object may indicate an estimated relationalsignificance of the external data object to an environment state of thedata interaction platform. In some embodiments, the one or more objectintegration parameters for the external data object include anattribute-based absorption score for the external data object, where theattribute-based absorption score of the external data object may bedetermined based at least in part on individual absorption scores ofdata objects whose individual attributes are deemed sufficiently similarto the individual attributes of the external data object, e.g., whoseindividual attributes have a similarity score with respect to theexternal data object that exceeds a threshold similarity score.

At step/operation 3602, the external integration engine 114 generatesone or more unintegrated data object relationships based at least inpart on the one or more object integration parameters. In someembodiments, an unintegrated data object relationship is a relationshipbetween the external data object and at least one of the group ofintegrated data objects. In some embodiments, each unintegrated dataobject relationship is associated with one or more related data objects,where the one or more related data objects associated with anunintegrated data object relationship may include the external dataobject and at least one of the group of integrated data objects. In someembodiments, generating the one or more unintegrated data objectrelationships includes mapping the one or more object integrationparameters to a relationship extrapolation space of the group ofintegrated data objects, wherein the relationship extrapolation space iscomprising one or more input dimensions associated with the one or moreobject integration parameters and one or more output dimensionsassociated with one or more candidate data object relationship types;and determining the one or more unintegrated data object relationshipsbased at least in part on the relationship extrapolation space. In someembodiments, generating the one or more relational absorption parametersfor an unintegrated data object relationship of the one or moreunintegrated data object relationships includes generating the one ormore relational absorption parameters based at least in part on the oneor more object integration parameters generated in step/operation 3601.

In some embodiments, determining the one or more unintegrated dataobject relationships based at least in part on the relationshipextrapolation space includes utilizing an unsupervised machine learningmodel (e.g., a clustering machine learning, a K-nearest-neighbor machinelearning model, and/or the like) defined by the input space and outputspace of the relationship extrapolation space. In some embodiments,determining the one or more unintegrated data object relationships basedat least in part on the relationship extrapolation space includesutilizing a supervised machine learning model (e.g., a neural networkmachine learning model) defined by the input space and output space ofthe relationship extrapolation space. In some embodiments, determiningthe one or more unintegrated data object relationships based at least inpart on the relationship extrapolation space includes utilizing anonline machine learning model (e.g., a follow-the-regularized-leadermachine learning model) defined by the input space and output space ofthe relationship extrapolation space.

In some embodiments, step/operation 3602 for a particular external dataobject that is a digital document may be performed in accordance withthe process depicted in FIG. 37. The process depicted in FIG. 37 beginsat step/operation 3701 when the external integration engine 114 receivesthe digital document. The digital document may be an emailcommunication, a word file, etc. The digital document may be in aregular text format, rich text format, image format, etc. The digitaldocument may be uploaded to the data interaction platform computingentity 106, identified (e.g., downloaded) by the external integrationengine 114 based on monitoring end-user activity, and/or generated bythe external integration engine 114 based on monitoring end-useractivity.

At step/operation 3702, the external integration engine 114 performstext-preprocessing on the digital document to generate relevant featuresfrom the digital document. The text pre-processing performed by theexternal integration engine 114 may include at least one oftokenization, stop-word removal, term-frequency-inverse-domain-frequency(TF-IDF) modeling, word embedding modeling. In some embodiments, TF-IDFmodeling may be performed based on a corpus of data that include textualdata associated with other data objects modeled by the data interactionplatform computing entity 106. In some embodiments, TF-IDF modeling maybe performed based on a vocabulary of modeled terms defined byconfiguration parameters of the data interaction platform computingentity 106. In some embodiments, embedding features may be determinedbased on detected feature patterns of other data objects modeled by thedata interaction platform computing entity 106 and/or based onconfiguration parameters of the data interaction platform computingentity 106.

At step/operation 3703, the external integration engine 114 performsfeature modeling on the features generated at step/operation 3702.Feature modeling may involve utilizing one or more machine learningmodels, such as one or more supervised machine learning models and/orone or more unsupervised machine learning models. In some embodiments,the machine learning models utilized for feature modeling include aconvolutional machine learning model. In some of the embodimentsutilizing a convolutional machine learning model to perform featuremodeling, the kernels of the convolutional machine learning model may bedefined based on data relationships defined based on relationalawareness scores generated by the relational awareness modeling engine111. For example, the relational awareness modeling engine 111 maydetermine a strong relationship between person data objects of the typeexecutive and event objects of the type earning call and thus look forcombinations of the terms associated with the two noted data objectsusing a particular feature extraction kernel of a convolutional neuralnetwork.

At step/operation 3704, the external integration engine 114 performsoutput generation based on the feature modeling data generated instep/operation 3703. The generated output includes one or moreunintegrated data object relationships each defined by one or moreunintegrated data object relationship parameters, where the unintegrateddata object relationship parameters for each unintegrated data objectrelationship are determined based on the feature modeling dataassociated with the digital document. The generated output may furtherinclude text summarization data, document classification data, andsentiment detection data.

At step/operation 3603, the external integration engine 114 generatesone or more relational awareness scores for each unintegrated dataobject relationship generated in step/operation 3603. In someembodiments, for each unintegrated data object relationship of the oneor more unintegrated data object relationships, the external integrationengine 114 generates one or more relational absorption parameters forthe unintegrated data object relationship, and generates, based at leastin part on the one or more relational absorption parameters, arespective relational awareness score for the unintegrated data objectrelationship with respect to each related data object of the one or morerelated data objects that is associated with the unintegrated dataobject relationship. In some embodiments, by generating data objectrelationships between a new data object and existing data objects of arelationally aware database as well as generating relational awarenessscores for the newly generated data object relationships, the externalintegration engine 114 performs external integration of the new dataobject into the relationally aware database in an effective andefficient manner. In some embodiments, a continuous altering ofrelationships creates a ripple effect, where the change, addition, orremoval of an object can create new relationships, create newrelationship parameters for existing relationships, and impact existingrelationship parameters.

In some embodiments, generating each relational awareness score for anunintegrated data object relationship includes mapping the one or morerelational absorption parameters for the unintegrated data objectrelationship to a relational score extrapolation score for the group ofintegrated data object relationships, wherein the relational scoreextrapolation space is comprising one or more input dimensionsassociated with the one or more relational absorption parameters and oneor more output dimensions associated with one or more relationalawareness parameters; and determining the relational awareness scorebased at least in part on the one or more relational awarenessparameters. In some embodiments, determining each relational awarenessscore for an unintegrated data object relationship based at least inpart on the relational score extrapolation space includes utilizing asupervised machine learning model (e.g., a neural network machinelearning model) defined by the input space and output space of therelationship extrapolation space. In some embodiments, determining eachrelational awareness score for an unintegrated data object relationshipbased at least in part on the relational score extrapolation spaceincludes utilizing a supervised machine learning model (e.g., a neuralnetwork machine learning model) defined by the input space and outputspace of the relationship extrapolation space. In some embodiments,determining each relational awareness score for an unintegrated dataobject relationship based at least in part on the relational scoreextrapolation space includes utilizing an online machine learning model(e.g., a follow-the-regularized-leader machine learning model) definedby the input space and output space of the relationship extrapolationspace.

VI. CONCLUSION

Thus, particular embodiments of the subject matter have been described.Other embodiments are within the scope of the following claims. In somecases, the actions recited in the claims can be performed in a differentorder and still achieve desirable results. In addition, the processesdepicted in the accompanying figures do not necessarily require theparticular order shown, or sequential order, to achieve desirableresults, unless described otherwise. In certain implementations,multitasking and parallel processing may be advantageous. Manymodifications and other embodiments will come to mind to one skilled inthe art to which this disclosure pertains having the benefit of theteachings presented in the foregoing descriptions and the associateddrawings. Therefore, it is to be understood that the disclosure is notto be limited to the specific embodiments disclosed and thatmodifications and other embodiments are intended to be included withinthe scope of the appended claims. Although specific terms are employedherein, they are used in a generic and descriptive sense only and notfor purposes of limitation.

The invention claimed is:
 1. A computer-implemented method for generating a data model with dynamic relational awareness for a data interaction platform comprising a plurality of data object relationships between a plurality of data objects, the computer-implemented method comprising: for each data object of the plurality of data objects: generating an individual absorption score, wherein the individual absorption score for the data object indicates an estimated relational awareness tendency of the data object given one or more individual attributes of the data object, generating a hierarchical absorption score, wherein the hierarchical absorption score for the data object is determined based at least in part on each individual absorption score for a parent data object that is a hierarchical parent of the data object, generating an operational absorption score, wherein the operational absorption score for the data object is determined based at least in part on each individual absorption score for a related data object that is operationally related to the data object, and generating an overall absorption score based at least in part on the individual absorption score for the data object, the hierarchical absorption score for the data object, and the operational absorption score for the data object; for each data object relationship, generating an environment-based absorption score, wherein the environment-based absorption score for the data object relationship indicates an estimated relational significance of the data object relationship given an environment state of the data interaction platform; and for each data object relationship that is associated with a plurality of related data objects, generating a relational awareness score for each of the plurality of related data objects associated with the data object relationship, wherein the relational awareness score for the data object relationship with respect to a particular related data object is determined based at least in part on the overall absorption score of the particular related data object and the environment-based absorption score for the data object relationship.
 2. The computer-implemented method of claim 1, wherein generating the individual absorption score for a particular data object of the plurality of data objects comprises: generating one or more absorption metrics for the particular data object based at least in part on the one or more object attributes of the particular data object; mapping the one or more absorption metrics to an individual absorption space; and generating the individual absorption score based at least in part on the individual absorption space.
 3. The computer-implemented method of claim 1, wherein the one or more parent data objects for a particular data object comprise a hierarchical meta-type of the particular data object, wherein the hierarchical meta-type of the particular data object indicates whether the particular data object is comprising one or more related hierarchical meta-type designations of a plurality of predefined hierarchical meta-type designations.
 4. The computer-implemented method of claim 3, wherein the plurality of predefined hierarchical meta-type designations comprises: a first predefined hierarchical meta-type designation associated with living real-world entities, a second predefined hierarchical meta-type designation associated with non-living-object real-world entities, a third predefined hierarchical meta-type designation associated with location-defining real-world entities, a fourth predefined hierarchical meta-type designation associated with time-defining real-world entities, a fifth predefined hierarchical meta-type designation associated with communication-defining entities, a sixth predefined hierarchical meta-type designation associated with group-defining entities, and a seventh predefined hierarchical meta-type designation associated with knowledge-defining entities.
 5. The computer-implemented method of claim 1, wherein the one or more related data objects for a particular data object comprise one or more user-defining objects associated with the particular data object and one or more access-defining data objects associated with the particular data object.
 6. The computer-implemented method of claim 5, wherein the one or more user-defining objects associated with the particular data object comprise one or more primary user-defining objects associated with the particular data object and one or more collaborator user-defining objects associated with the particular data object.
 7. The computer-implemented method of claim 5, wherein the one or more access-defining data objects associated with the particular data object comprise one or more sharing space data objects associated with the particular data object.
 8. The computer-implemented method of claim 1, wherein the environment state of the data interaction platform is selected from a plurality of candidate environment states of the data interaction platform.
 9. The computer-implemented method of claim 8, wherein the plurality of candidate environment states of the data interaction platform comprises one or more private environment states, one or more professional environment states, one or more leisure environment state, and one or more public environment states.
 10. The computer-implemented method of claim 1, further comprising: performing a data retrieval query in relation to the data interaction platform using the data model.
 11. The computer-implemented method of claim 10, wherein performing the data retrieval query comprises: identifying one or more filtering parameters associated with the data retrieval query; generating a query relevance score for each data object relationship of the plurality of data object relationships based at least in part on one or more filtering parameters and each relational awareness score associated with the data object relationship; and generating a query output based at least in part on each query relevance score for a data object relationship of the plurality of data object relationships.
 12. The computer-implemented method of claim 1, wherein: generating the overall absorption score for a particular data object of the plurality of data objects is performed further based at least in part on an attribute-based absorption score for the particular data object, and the attribute-based absorption score for the particular data object is performed based at least in part on each individual absorption score for a similar data object of the plurality of data objects whose respective individual attributes are determined to be sufficiently similar to the one or more object attributes of the particular data object.
 13. The computer-implemented method of claim 1, further comprising: detecting a modification of a first data object of the plurality of data objects; and in response to detecting the modification, updating each relational awareness score associated with the first data object.
 14. An apparatus for generating a data model with dynamic relational awareness for a data interaction platform comprising a plurality of data object relationships between a plurality of data objects, the apparatus comprising at least one processor and at least one memory including program code, the at least one memory and the program code configured to, with the processor, cause the apparatus to at least: for each data object of the plurality of data objects: generate an individual absorption score, wherein the individual absorption score for the data object indicates an estimated relational awareness tendency of the data object given one or more individual attributes of the data object, generate a hierarchical absorption score, wherein the hierarchical absorption score for the data object is determined based at least in part on each individual absorption score for a parent data object that is a hierarchical parent of the data object, generate an operational absorption score, wherein the operational absorption score for the data object is determined based at least in part on each individual absorption score for a related data object that is operationally related to the data object, and generate an overall absorption score based at least in part on the individual absorption score for the data object, the hierarchical absorption score for the data object, and the operational absorption score for the data object; for each data object relationship, generate an environment-based absorption score, wherein the environment-based absorption score for the data object relationship indicates an estimated relational significance of the data object relationship given an environment state of the data interaction platform; and for each data object relationship that is associated with a plurality of related data objects, generate a relational awareness score for each of the plurality of related data objects associated with the data object relationship, wherein the relational awareness score for the data object relationship with respect to a particular related data object is determined based at least in part on the overall absorption score of the particular related data object and the environment-based absorption score for the data object relationship.
 15. The apparatus of claim 14, wherein generating the individual absorption score for a particular data object of the plurality of data objects comprises: generating one or more absorption metrics for the particular data object based at least in part on the one or more object attributes of the particular data object; mapping the one or more absorption metrics to an individual absorption space; and generating the individual absorption score based at least in part on the individual absorption space.
 16. The apparatus of claim 14, wherein the one or more parent data objects for a particular data object comprise a hierarchical meta-type of the particular data object, wherein the hierarchical meta-type of the particular data object indicates whether the particular data object is comprising one or more related hierarchical meta-type designations of a plurality of predefined hierarchical meta-type designations.
 17. The apparatus of claim 16, wherein the plurality of predefined hierarchical meta-type designations comprises: a first predefined hierarchical meta-type designation associated with living real-world entities, a second predefined hierarchical meta-type designation associated with non-living-object real-world entities, a third predefined hierarchical meta-type designation associated with location-defining real-world entities, a fourth predefined hierarchical meta-type designation associated with time-defining real-world entities, a fifth predefined hierarchical meta-type designation associated with communication-defining entities, a sixth predefined hierarchical meta-type designation associated with group-defining entities, and a seventh predefined hierarchical meta-type designation associated with knowledge-defining entities.
 18. The apparatus of claim 14, wherein the one or more related data objects for a particular data object comprise one or more user-defining objects associated with the particular data object and one or more access-defining data objects associated with the particular data object.
 19. The apparatus of claim 18, wherein the one or more user-defining objects associated with the particular data object comprise one or more primary user-defining objects associated with the particular data object and one or more collaborator user-defining objects associated with the particular data object.
 20. A computer program product for generating a data model with dynamic relational awareness for a data interaction platform comprising a plurality of data object relationships between a plurality of data objects, the computer program product comprising at least one non-transitory computer-readable storage medium having computer-readable program code portions stored therein, the computer-readable program code portions configured to: for each data object of the plurality of data objects: generate an individual absorption score, wherein the individual absorption score for the data object indicates an estimated relational awareness tendency of the data object given one or more individual attributes of the data object, generate a hierarchical absorption score, wherein the hierarchical absorption score for the data object is determined based at least in part on each individual absorption score for a parent data object that is a hierarchical parent of the data object, generate an operational absorption score, wherein the operational absorption score for the data object is determined based at least in part on each individual absorption score for a related data object that is operationally related to the data object, and generate an overall absorption score based at least in part on the individual absorption score for the data object, the hierarchical absorption score for the data object, and the operational absorption score for the data object; for each data object relationship, generate an environment-based absorption score, wherein the environment-based absorption score for the data object relationship indicates an estimated relational significance of the data object relationship given an environment state of the data interaction platform; and for each data object relationship that is associated with a plurality of related data objects, generate a relational awareness score for each of the plurality of related data objects associated with the data object relationship, wherein the relational awareness score for the data object relationship with respect to a particular related data object is determined based at least in part on the overall absorption score of the particular related data object and the environment-based absorption score for the data object relationship. 